text:
\section{Introduction}
In recent years, text recognition has attracted extensive attention due to its wide applications such as autonomous driving \cite{zhang2021character,qian2015robust}, document retrieval \cite{yang2019simple,maekawa2019improving}, signature identification \cite{ren2020st,poddar2020offline}, etc. However, the existing text recognition methods mainly focus on English texts \cite{shi2016end,shi2018aster,cheng2017focusing,cheng2018aon,bai2018edit,luo2019moran,li2019show,qiao2020seed,he2016reading,wan2020textscanner,sheng2019nrtr,wan2020vocabulary} while ignoring the huge market of Chinese text recognition (CTR). Specifically, Chinese is the most spoken language across the world with 1.31 billion speakers, meaning that CTR and its downstream tasks will certainly have a crucial impact on this population.

Based on our observations, we summarize three potential reasons for the scarce attention to Chinese text recognition: 1) \textbf{Lack of reasonable dataset construction standards.} Ideally, based on the given quadrangle boxes, we can crop the text regions along the annotation points and then rectify it to a horizontal-oriented image, which can effectively eliminate useless background areas compared to those methods directly using the minimum circumscribed horizontal boxes. Therefore, different cropping methods will lead to unfair comparison. Also, datasets collected from different environments show much variance in appearance. Finding a reasonable splitting strategy is also beneficial for more effective research. Hence, the standard of dataset construction should be taken into consideration. 2) \textbf{Lack of unified evaluation protocols.} Generally, when evaluating recognition models in English text datasets, researchers usually convert the uppercase to lowercase by default. However, there do not exist any unified evaluation protocols for CTR. For example, researchers may feel confused that whether full-width and half-width characters, simplified and traditional characters should be treated as the same character. Additionally, the evaluation metrics (\textit{e.g.}, normalized edit distance and accuracy rate) are inconsistent across CTR papers. Therefore, unified evaluation protocols are urgently needed for fairly assessing CTR methods. 3) \textbf{Lack of experimental results of the existing baselines.} The existing text recognition methods are mainly evaluated on English text datasets like IIIT5K \cite{mishra2012top}, IC03 \cite{lucas2005icdar}, IC13 \cite{karatzas2013icdar}, etc. Although few methods attempt to experiment on Chinese datasets, the details of dataset construction are not clearly explained in the corresponding papers, which makes it difficult for other researchers to use as CTR baselines.

\begin{figure*}[t]

    \centering
    \includegraphics[width=0.90\textwidth]{image/fig1.pdf}
    \caption{Some samples in the scene, web, document, and handwriting datasets.}
    \label{fig:dataset introduction}

\end{figure*}

In this paper, we seek to construct a benchmark for CTR to fill this gap. We first derive the existing CTR datasets from the public competitions, papers, and projects, resulting in four categories, \textit{i.e.}, scene, web, document, and handwriting (Some examples of each dataset are shown in Figure \ref{fig:dataset introduction}). We also demonstrate the rationality of the dataset construction in terms of each category's recognizability which is calibrated by humans. Then we manually divide each dataset into training, validation, and testing sets at a reasonable ratio. The validation set aims to fairly compare the existing methods, \textit{i.e.}, making sure that the best hyper-parameters is chosen based on the validation set so as to avoid testing-set-oriented tuning. Besides, we reproduce the results of some representative text recognition methods on the collected dataset as baselines. The experimental results indicate that some SOTA methods originally proposed for English texts do not perform as well on CTR datasets as on English datasets. Through analysis, a possible reason is that some characteristics of Chinese texts put obstacles to the existing methods. In view of the complex inner structures of Chinese characters, we introduce the radical-level supervision in a multi-task fashion for better recognition. Overall, our contributions can be listed as follows:

\begin{itemize}
    \item We manually collect CTR datasets from public competitions, papers, and projects. Then we divide them into four categories, \textit{i.e.}, scene, web, document, and handwriting datasets. We further split each dataset into training, evaluation, and testing sets at a reasonable ratio.
    \item We standardize the evaluation protocols to fairly compare existing text recognition methods.
    \item Based on the collected datasets and standardized evaluation protocols, we reproduce the results of a series of baselines, then analyze the performance of the baselines in detail.
    \item We introduce the radical-level supervision in CTR to generally improve the performance of existing attention-based recognizers.

\end{itemize}

\begin{figure*}[t]
    \centering
    \includegraphics[width=1.00\textwidth]{image/fig2.png}
    \caption{The preliminary knowledge of representing Chinese characters.}
    \label{fig:structure_and_stroke}
\end{figure*}

\section{Preliminaries}

\subsection{Hierarchical Representations for Chinese Characters}
\label{2.1}
Here we introduce three representations of Chinese characters (see the example ``\ch{奇}'' in Figure \ref{fig:structure_and_stroke}(a)), \textit{i.e.}, in character level, radical level, and stroke level.

\textbf{Character level.} According to the Chinese national standard GB18030-2005\footnote{https://zh.wikipedia.org/wiki/GB\_18030}, the number of Chinese characters is 70,244 in total, where 3,755 characters are Level-1 commonly-used characters.

\textbf{Radical level.} According to the Unicode standards for idiographic  description characters\footnote{https://unicode.org/charts/PDF/U2FF0.pdf}, there are 12 radical structures (see Figure \ref{fig:structure_and_stroke}(b)) and 514 radicals in the Level-1 commonly-used Chinese characters. For the 3,755 commonly-used Chinese characters, the radical-level representation can effectively reduce the size of alphabet from 3,755 to 526.

\textbf{Stroke level.} According to Unicode Han Database\footnote{http://www.unicode.org}, each Chinese character can be decomposed into a stroke sequence. There are five basic categories of strokes (\textit{e.g.}, horizontal, vertical, left-falling, right-falling, and turning), each of which contains several instances (see Figure \ref{fig:structure_and_stroke}(c)).

\subsection{Characteristics of Chinese Texts}
\label{characteristicsOfchinese}

It is acknowledged in the community that Chinese texts are harder to recognize compared with English texts \cite{chen2021zero,wang2018denseran}. To explore the intrinsic reasons, here we analyze the characteristics of Chinese texts that are distinct from English texts:

\textbf{Large amount of characters.} According to the national standard GB18030-2005, the number of Chinese characters is 70,244 (including 3,755 commonly-used Level-1 characters). It is much larger than the scale of English characters, which only contains 26 uppercase and 26 lowercase letters (see Figure \ref{fig:five properties}(a)). On one hand, performing large-scale classification is essentially a difficult task \cite{deng2010does}. On the other hand, when experimenting on CTR datasets, recognizers are likely to encounter challenging zero-shot problems, \textit{i.e.}, the characters to be tested may be absent from the training set.

\textbf{Similar appearance.} Compared with English letters, there are considerable Chinese character clusters with similar appearance (see Figure \ref{fig:five properties}(b)). For example, the difference between ``\ch{戌}'' and ``\ch{戍}'' only lies in a tiny stroke. They are hard to distinguish even for human eyes, which indeed burdens the existing text recognition methods.

\textbf{Complicated sequential patterns.} We observe that most samples in the existing English benchmarks are at the word level. Since there is a space between two English words, humans tend to split them apart during the labeling process for detection. Besides, there are inherent statistical patterns within English words (\textit{e.g.}, ``abl'' is more likely followed by ``e'' ), thus helping recognizers better capture the sequential patterns. On the contrary, Chinese texts are more likely to appear in phrases or sentences. Under this circumstance, there are complicated dependencies among Chinese characters in terms of the part of speech (see Figure \ref{fig:five properties}(c)), which indeed brings difficulties for recognizers to learn sequential patterns.

\textbf{Commonly-seen vertical texts.} Compared with English texts, Chinese texts are more likely to appear vertically due to the commonly-used traditional couplets or signboards in natural scenes. On the contrary, there are few vertical English texts due to people's inherent reading habits (see Figure \ref{fig:five properties}(d)).

\textbf{Complex inner structures}. As introduced in \ref{2.1}, different from single-component characters in English, most of Chinese characters are multi-component (see Figure \ref{fig:structure_and_stroke}(a)), \textit{i.e.}, each Chinese character can be decomposed into several radicals organized by radical structures. More complex inner structures makes Chinese character classification more difficult.

\begin{figure*}[t]
    \centering
    \includegraphics[width=1.00\textwidth]{image/fig3.png}
    \caption{Characteristics of Chinese texts that are distinct from English texts.}
    \label{fig:five properties}
\end{figure*}

\section{Datasets}

\subsection{Details of Datasets}
\textbf{Scene dataset.}
From the competitions, papers, and projects, we have derived a series of scene datasets, including RCTW \cite{shi2017icdar2017}, ReCTS \cite{zhang2019icdar}, LSVT \cite{sun2019icdar}, ArT \cite{chng2019icdar2019}, and CTW \cite{yuan2019large}. The details of each dataset are introduced as follows:

\begin{itemize}
    \item \textbf{RCTW} \cite{shi2017icdar2017}: It provides 12,263 annotated Chinese text images from natural scenes. We derive 44,420 text images from the training set and use them in our benchmark. The testing set of RCTW is not used as the text labels are not available.

    \item \textbf{ReCTS} \cite{zhang2019icdar}: It provides 25,000 annotated street-view Chinese text images, mainly derived from signboards. We only adopt the training set and crop 107,657 text samples in total for our benchmark.

    \item \textbf{LSVT} \cite{sun2019icdar}: It is a large scale Chinese and English scene text dataset, providing 50,000 full-labeled (polygon boxes and text labels) and 400,000 partial-labeled (only one text instance each image) samples. We only utilize the full-labeled training set and crop 243,063 text images for our benchmark.

    \item \textbf{ArT} \cite{chng2019icdar2019}: It contains text samples captured in natural scenes with various text layouts (\textit{e.g.}, rotated text and curved texts). Here we obtain 49,951 cropped text images from the training set, and use them in our benchmark.

    \item \textbf{CTW} \cite{yuan2019large}: It contains annotated 30,000 street view images with rich diversity including raised texts, occluded texts, poorly-illuminated texts, etc. Also, it provides not only character boxes and labels, but also character attributes like background complexity, appearance, etc. Here we crop 191,364 text images from both the training and testing sets.
\end{itemize}

We combine all the above-mentioned datasets, resulting in 636,455 text samples. We randomly shuffle these samples and split them at a ratio of 8:1:1, leading to 509,164 samples for training, 63,645 samples for validation, and 63,646 samples for testing.

\textbf{Web dataset.} To collect the web dataset, we utilize MTWI \cite{he2018icpr2018} that contains 20,000 Chinese and English web text images from 17 different categories on the \textit{Taobao} website. The text samples are appeared in various scenes, typography and designs. We derive 140,589 text images from the training set, and manually divide them at a ratio of 8:1:1, resulting in 112,471 samples for training, 14,059 samples for validation, and 14,059 samples for testing.

\textbf{Document dataset.} We use the public repository Text Render\footnote{https://github.com/Sanster/text\_renderer} to generate some document-style synthetic text images. More specifically, we uniformly sample the length of text varying from 1 to 15. The corpus comes from \textit{Wiki}, \textit{Films}, \textit{Amazon}, and \textit{Baike}. The dataset contains 500,000 in total and is randomly divided into training, validation, and testing sets with a proportion of 8:1:1 (400,000 \textit{v.s.} 50,000 \textit{v.s.} 50,000).

\textbf{Handwriting dataset.} We collect the handwriting dataset based on SCUT-HCCDoc \cite{zhang2020scut}, which captures the Chinese handwritten images with cameras in unconstrained environments. Following the official settings, we derive 93,254 text images for training and 23,389 for testing. To pursue more rigorous research, we manually split the original training set into two sets at a ratio of 4:1, resulting in 74,603 samples for training and 18,651 samples for validation. For convenience, we continue to use the original testing set.

\textbf{Licenses of adopted datasets.} For the datasets RCTW \cite{shi2017icdar2017}, ReCTS \cite{zhang2019icdar}, ArT \cite{chng2019icdar2019}, LSVT \cite{sun2019icdar}, and MTWI \cite{he2018icpr2018}, there were no relevant open-source licenses when these datasets were published. Therefore, we have contacted the owners of these datasets, and confirmed that they can be used for academic research. The dataset CTW \cite{yuan2019large} uses the license Attribution-NonCommercial-ShareAlike 4.0 International, termed CC BY-NC-SA 4.0 \footnote{https://creativecommons.org/licenses/by-nc-sa/4.0/}, where users are authorized to copy and redistribute the material in any medium or format. In the GitHub repository of SCUT-HCCDoc \cite{zhang2020scut} \footnote{https://github.com/HCIILAB/SCUT-HCCDoc\_Dataset\_Release}, the authors have stated that this dataset can only be used for non-commercial research purpose. Also, we have contacted the dataset's authors and obtained permission for academic research.

\begin{figure}[t]
\centering
\begin{minipage}[t]{0.49\textwidth}
\centering
\includegraphics[width=7.0cm]{image/fig4.png}
\caption{The length distribution.}\label{fig:length}
\end{minipage}
\begin{minipage}[t]{0.49\textwidth}
\centering
\includegraphics[width=7.0cm]{image/fig5.png}
\caption{The aspect ratio distribution.}\label{fig:ratio}
\end{minipage}
\end{figure}

\begin{table*}[t]
\small
\caption{The statistical results of the alphabet size and the amount of characters. ``Chinese'', ``All'', ``Proportion'' denote the number of Chinese characters, all characters, the proportion of Chinese characters, respectively. ``ZS'' represents the number of zero-shot characters in the testing sets.
}
\centering
\scalebox{0.98}{\begin{tabular}{c p{1.2cm}<{\centering} p{1.1cm}<{\centering} p{1.1cm}<{\centering} p{1.2cm}<{\centering}
p{0.5cm}<{\centering}
p{2.2cm}<{\centering} p{1.6cm}<{\centering}}
\toprule
 \multirowcell{2}{Dataset} &  \multirowcell{2}{Train} & \multirowcell{2}{Valid} & \multirowcell{2}{Test} &
 \multirowcell{2}{Alphabet \\ Size} & \multirowcell{2}{ZS}&
 \multicolumn{2}{c}{Amount of Characters}\\
 \cmidrule(r){7-8}
~ & ~ & ~ & ~ & ~ & ~ & \makecell[c]{Chinese/All} & \makecell[c]{Proportion} \\
\midrule
Scene & 509,164 & 63,645 & 63,646  & 5,880 & 109 & 2,188K / 3,207K & 68.2\% \\
Web & 112,471 & 14,059 & 14,059  & 4,402 & \; 94 & \ \; 315K / 703K & 44.9\% \\
Document & 400,000 & 50,000 & 50,000 & 4,865 & \; 25 & 3,386K / 3,997K & 84.7\% \\
Handwriting & \; 74,603 & 18,651 & 23,389  & 6,105 & 253  & \ \; 983K / 1,154K & 85.3\% \\

\bottomrule
\end{tabular}
}
\label{tab:alphabet and character}
\end{table*}

\begin{figure*}[t]
    \centering
    \includegraphics[width=1.00\textwidth]{image/fig6.png}
    \caption{Steps to preprocess the collected datasets.}
    \label{fig:steps}
\end{figure*}

\subsection{Preprocessing}
Here we propose four steps to preprocess the collected four categories of datasets: \textbf{1) Reserve the text images that contain other languages.} We observe that the existing CTR datasets mainly comprise Chinese characters, meanwhile containing a few English characters as well as other languages (see Figure \ref{fig:steps}(a)). Considering the language distributions of text images in natural scenes, we decide to reserve the samples with characters in other languages. \textbf{2) Remove the samples annotated as ``\#\#\#''.} According to the labeling standards of some datasets (\textit{e.g.}, RCTW \cite{shi2017icdar2017} and ReCTS \cite{zhang2019icdar}), the illegible text images are annotated as ``\#\#\#'' (see Figure \ref{fig:steps}(b)). We observe that there exists heavy blur or occlusion within these samples, which are hard to recognize even for human eyes. Considering that these samples may bring noises to the training process, we decide to remove them from the datasets. \textbf{3) Construct separate validation sets.} Through observations, the existing English text recognition benchmarks usually lack the validation sets. Practically, the testing sets are not available during the training stage and the best model should be chosen according to the performance on the validation sets. Here we take the validation sets into account in pursuit of more rigorous research (see Figure \ref{fig:steps}(c)). \textbf{4) Only collect samples with available text labels.} We observe that the text labels of many testing sets are not publicly available, especially for those competition datasets. Under this circumstance, we only use those samples with available annotated text labels.

\subsection{Analysis of Datasets}

\textbf{Alphabet size and amount of characters.} In Table \ref{tab:alphabet and character}, we can see that the alphabet size and the proportion of Chinese characters vary across the four datasets. For example, most of the web texts are Chinese advertisements with fixed phrase expressions, thus containing fewer characters in the alphabet (4,402 characters). Besides, the web dataset comprises many telephone numbers and English websites, leading to the least proportion of Chinese characters among the four datasets (44.9\%). For the handwriting dataset, most texts are Chinese ancient poems, which contain more rarely-used characters than the other datasets, thus resulting in the largest alphabet (6,105 characters). As a result, the testing set of the handwriting dataset contains the most zero-shot characters (253 zero-shot characters) that are absent from the training set, which further increases the difficulty of recognition.

\textbf{Distributions of text length and aspect ratio}
Figure \ref{fig:length} and Figure \ref{fig:ratio} illustrate the distributions of text length and aspect ratio (\textit{i.e.}, the ratio of width to height). From these figures, we observe that long texts (\textit{e.g.}, length $\ge 10$) appear more frequently in the handwriting dataset, which brings difficulties to the baselines. On the contrary, the text in the scene and web datasets are relatively short, perhaps considering the reading efficiency of passengers, customers, etc. For the ratio distribution, we observe that the scene dataset has more vertical text than others (ratio $\le$ 1) due to the commonly-used couplets and signboards in the wild. By contrast, the handwriting dataset comprises more horizontal texts.

\textbf{Character and word frequency.} As shown in Table \ref{tab:frequency}, we analyze the frequency of characters and words\footnote{We use jieba to split texts into words. The homepage of jieba: https://github.com/fxsjy/jieba} in each dataset and observe some interesting phenomena. For instance, as there are many Chinese brands or state-owned enterprises in natural scenes, the characters \begin{CJK}{UTF8}{gbsn}``中'', ``国'', \end{CJK} and the word \begin{CJK}{UTF8}{gbsn}``中国''\end{CJK} (China) appear frequently in the scene dataset. Additionally, the web dataset contains many high-frequency advertising terms like \begin{CJK}{UTF8}{gbsn}``包邮'' (free shipping), ``正品'' (certified products), etc.\end{CJK} Since most of the samples in the document and handwriting datasets are Chinese sentences, the auxiliary word \begin{CJK}{UTF8}{gbsn}``的''\end{CJK} appears most frequently. In particular, in the handwriting dataset, since some text images are cropped from diaries, there are many characters or words referring to people like \begin{CJK}{UTF8}{gbsn}``我'' (I), ``你'' (you), ``我们'' (we), etc.\end{CJK}

\begin{table*}[t]
\small
\caption{Top six high-frequency characters and words in each dataset (frequency in parentheses).
}
\centering
\scalebox{0.95}{\begin{tabular}{c | r r r r r r r r}
\toprule
 \multirow{2}*{Scene} & \begin{CJK}{UTF8}{gbsn}电\footnotesize{(26,986)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}中\footnotesize{(26,564)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}店\footnotesize{(18,644)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}国\footnotesize{(17,835)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}大\footnotesize{(17,819)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}话\footnotesize{(16,543)}\end{CJK}\\
~ & \begin{CJK}{UTF8}{gbsn}电话\footnotesize{(14,969)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}中国\footnotesize{(5,554)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}有限公司\footnotesize{(5,298)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}杭州\footnotesize{(3,793)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}热线\footnotesize{(3,612)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}中心\footnotesize{(3,505)}\end{CJK} \\
\midrule
 \multirow{2}*{Web} & \begin{CJK}{UTF8}{gbsn}品\footnotesize{(4,077)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}包\footnotesize{(3,104)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}电\footnotesize{(2,682)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}送\footnotesize{(2,597)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}一\footnotesize{(2,493)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}全\footnotesize{(2,413)}\end{CJK}\\
~ & \begin{CJK}{UTF8}{gbsn}包邮\footnotesize{(1,851)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}正品\footnotesize{(1,627)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}电子\footnotesize{(625)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}原装\footnotesize{(602)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}全国\footnotesize{(538)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}品质\footnotesize{(519)}\end{CJK}\\
\midrule
 \multirow{2}*{Document} & \begin{CJK}{UTF8}{gbsn}的\footnotesize{(149,351)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}是\footnotesize{(60,534)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}不\footnotesize{(54,367)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}一\footnotesize{(51,760)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}这\footnotesize{(38,080)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}有\footnotesize{(36,839)}\end{CJK}\\
~ & \begin{CJK}{UTF8}{gbsn}不错\footnotesize{(8,411)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}没有\footnotesize{(7,382)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}故事\footnotesize{(7,263)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}这个\footnotesize{(7,247)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}可以\footnotesize{(5,938)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}一个\footnotesize{(5,263)}\end{CJK}\\
\midrule
 \multirow{2}*{Handwriting} & \begin{CJK}{UTF8}{gbsn}的\footnotesize{(31,845)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}我\footnotesize{(17,543)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}不\footnotesize{(15,663)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}一\footnotesize{(15,028)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}是\footnotesize{(13,257)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}你\footnotesize{(11,773)}\end{CJK} \\
~ & \begin{CJK}{UTF8}{gbsn}自己\footnotesize{(2,474)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}抄写\footnotesize{(1,751)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}我们\footnotesize{(1,739)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}一个\footnotesize{(1,270)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}不是\footnotesize{(1,171)}\end{CJK} & \begin{CJK}{UTF8}{gbsn}如果\footnotesize{(973)}\end{CJK}\\
\bottomrule
\end{tabular}
}
\label{tab:frequency}
\end{table*}

\begin{table*}[t]
\small
\caption{The statistical results of recognizability calibrated by humans (BG for background).
}
\centering
\scalebox{0.97}{\begin{tabular}{c p{1.6cm}<{\centering}   p{2.4cm}<{\centering} p{2.2cm}<{\centering} p{1.6cm}<{\centering} p{1.7cm}<{\centering}}
\toprule
Dataset & Occlusion & Oblique or Curved & BG Confusion & Scribble & Blur \\
\midrule
Scene & \textbf{37(7.4\%)} & 81(16.2\%) & \textbf{93(18.6\%)} & \;\;\; 28(5.6\%) & \textbf{189(37.8\%)} \\
Web & 29(5.8\%) & \textbf{91(18.2\%)} & 67(13.4\%) & \;\;\; 11(2.2\%) & 106(21.2\%) \\
Document & \; 5(1.0\%) & 50(10.0\%) & \;\;\; 1(0.2\%) & \;\;\;\;\; 1(0.2\%) & \;\;\; 42(8.4\%) \\
Handwriting & 11(2.2\%) & \; 24(4.8\%) & \; 30(6.0\%) & \textbf{202(40.4\%)} & \;\;\; 35(7.0\%) \\
\bottomrule
\end{tabular}
}
\label{tab:visual experiment}
\end{table*}

\textbf{Recognizability calibration by humans.} To figure out the factors that impair the recognizability of text images in the four datasets, we invite 20 highly-educated\footnote{Ensure that the participant can recognize all well-written characters.} participants to conduct this experiment. We invite the participants to identify the corresponding reasons (multiple choices) that hamper the recognizability from different perspectives: 1) Occlusion (from foreground), 2) Oblique or Curved (instance-level), 3) Background Confusion (from background), 4) Scribble (character-level), 5) Blur (image source). If none of the reasons is satisfied, we consider the text image recognizable. Overall, we have 500 votes for each dataset (20 participants on 25 image samples per dataset). The statistics are demonstrated in Table \ref{tab:visual experiment}. We can see that, in the scene dataset, the votes of participants mainly focus on ``Occlusion'', ``Background Confusion'', and ``Blur'', indicating that it is the most complicated dataset for humans to recognize. The factors that impair the recognizability mostly come from the wild environment and the way of capturing text images. For the web dataset, the text appearance can be more various through controllable generation, resulting in the most votes for the ``Oblique or Curved'' selection. For the document dataset, the samples are relatively recognizable for humans and thus are less voted. In the handwriting dataset, participants vote the most for ``Scribble'', indicating that the stroke cohesion in the writing is also a basic factor that precludes the recognizability. From the observations above, we can find that the factors that impair the recognizability vary across datasets, and thus motivate us to investigate the performance on each dataset separately.

\section{Baselines}
\label{baseline}
Text recognition has achieved rapid progress over the last decade. According to the major characteristics, text recognition methods can be classified into several categories, including CTC-based methods, rectification-based methods, etc. From these categories, we select as baselines eight \textit{representative} methods, which are mostly used for comparison in the text recognition task.

\textbf{CRNN} (Shi \textit{et al.}, 2016) \cite{shi2016end} is a typical CTC-based method and it is widely used in industry. It sends the text image to a CNN to extract the image features, then adopts a two-layer LSTM to encode the sequential patterns. Finally, the output of LSTM is fed to a CTC~(Connectionist Temperal Classification)~\cite{graves2006connectionist} decoder to maximize the probability of all the paths towards the ground truth.

\textbf{ASTER} (Shi \textit{et al.}, 2018) \cite{shi2018aster} is a typical rectification-based method aiming at tackling irregular text images. It introduces a Spatial Transformer Network (STN)~\cite{jaderberg2015spatial} to rectify the given text image. Then the rectified text image is sent to a CNN and a two-layer LSTM to extract the features. In particular, ASTER takes advantage of the attention mechanism to predict the final text sequence.

\textbf{MORAN} (Luo \textit{et al.}, 2019)  \cite{luo2019moran} is a representative rectification-based method. It first adopts a multi-object rectification network~(MORN) to predict rectified pixel offsets in a weak supervision way (distinct from ASTER that utilizes STN). The output pixel offsets are further used for generating the rectified image, which is further sent to the attention-based decoder~(ASRN) for text recognition.

\textbf{SAR} (Li \textit{et al.}, 2019) \cite{li2019show} is a representative method that takes advantage of 2-D feature maps for more robust decoding. Different from CRNN, ASTER, and MORAN compressing the given image into a 1-D feature map, SAR adopts 2-D attention on the spatial dimension of the feature maps for decoding, resulting in a stronger performance in curved and oblique texts.

\textbf{SEED} (Qiao \textit{et al.}, 2020)  \cite{qiao2020seed} is a representative semantics-based method. It introduces a semantics module to extract global semantic embedding and utilize it to initialize the first hidden state of the decoder. Benefiting from introducing semantics prior to the recognition process, the decoder of SEED shows superiority in recognizing low-quality text images.

\textbf{MASTER} (Lu \textit{et al.}, 2021)
\cite{lu2021master} is an attention-based method that utilizes the self-attention mechanism to learn a more powerful and robust representation for distorted text images. Meanwhile, benefiting from the training parallelization and memory-cache mechanism, this method has a great training efficiency and a high-speed inference.

\textbf{ABINet} (Fang \textit{et al.}, 2021)
\cite{fang2021read} is an autonomous, bidirectional, and iterative method for scene text recognition. The autonomous principle means that the vision model and language model should be learned separately; the bidirectional principle is proposed to capture twice the amount of information; the iterative principle is designed to refine the prediction from visual and linguistic cues. In addition, the authors proposed a self-training method that enables ABINet to learn from unlabeled images.

\textbf{TransOCR} (Chen \textit{et al.}, 2021) \cite{chen2021scene} is one of the representative Transformer-based methods. It is originally designed to provide text priors for the super-resolution task. It employs ResNet-34 \cite{he2016deep} as the encoder and self-attention modules as the decoder. Distinct from the RNN-based decoders, the self-attention modules are more efficient to capture semantic features of the given text images.

Through the experimental results of baselines (the details will be introduced in Section \ref{experiments}), we observe that the performance of baselines on CTR datasets is not as good as that on English datasets due to the characteristics of Chinese texts (see Section \ref{characteristicsOfchinese}). In terms of complex inner structures of Chinese characters, we propose a Pluggable Radical-Aware Branch (PRAB), which can be inserted into any attention-based recognizer, to introduce the radical-level supervision in a multi-task fashion for better recognition. More details about PRAB are shown in Supplementary Materials.

\section{An Empirical Study}
\label{experiments}

\begin{table*}[t]
\small
\caption{The results of the baselines on four datasets. ACC / NED follows the percentage format and decimal format, respectively.
}
\centering
\scalebox{0.95}{\begin{tabular}{p{2.5cm}<{\centering} p{1.7cm}<{\centering} p{1.7cm}<{\centering} p{1.7cm}<{\centering} p{1.7cm}<{\centering} p{1cm}<{\centering} p{1cm}<{\centering}}
\toprule
\multirow{2}*{Method} & \multicolumn{4}{c}{Dataset} & \multirow{2}*{Parameters} & \multirow{2}*{FPS} \\
\cmidrule{2-5}
~  & Scene & Web & Document & Handwriting \\
\midrule
CRNN \cite{shi2016end} & 54.94 / 0.742 & 56.21 / 0.745 & 97.41 / 0.995 & 48.04 / \textbf{0.843} & \textbf{12.4M} & \textbf{751.0} \\

ASTER \cite{shi2018aster} & 59.37 / 0.801 & 57.83 / \textbf{0.782} & 97.59 / 0.995 & 45.90 / 0.819 & 27.2M & 107.3 \\

MORAN \cite{luo2019moran} & 54.68 / 0.710 & 49.64 / 0.679 & 91.66 / 0.984 & 30.24 / 0.651 & 28.5M & 301.5 \\

SAR \cite{li2019show} & 53.80 / 0.738 & 50.49 / 0.705 & 96.23 / 0.993 & 30.95 / 0.732 & 27.8M & 93.1 \\

SEED \cite{qiao2020seed} & 45.37 / 0.708 & 31.35 / 0.571 & 96.08 / 0.992 & 21.10 / 0.555 & 36.1M & 106.6 \\
MASTER \cite{lu2021master} & 62.14 / 0.763 &	53.42 / 0.704 & 82.69 / 0.957 & 18.52 / 0.504 & 62.8M & 16.3 \\
ABINet \cite{fang2021read} & 60.88 / 0.775 & 51.07 / 0.704 & 91.67 / 0.987 & 13.83 / 0.514 & 53.1M & 92.1 \\

TransOCR \cite{chen2021scene} & \textbf{67.81 / 0.817} & \textbf{62.74 / 0.782} & \textbf{97.86 / 0.996} & \textbf{51.67} / 0.835 & 83.9M & 164.6 \\

\bottomrule
\end{tabular}
}
\label{tab:new baseline result}
\end{table*}

\subsection{Experiments}

\textbf{Implementation details}. We adopt the off-the-shelf PyTorch implementations of CRNN \cite{shi2016end}, ASTER \cite{shi2018aster}, MORAN \cite{luo2019moran}, SAR \cite{li2019show}, SEED \cite{qiao2020seed}, MASTER \cite{lu2021master}, ABINet \cite{fang2021read} and TransOCR \cite{chen2021scene} on Github to reproduce the experimental results on the collected CTR datasets. All baseline experiments are conducted on an NVIDIA RTX 2080Ti GPU with 11GB memory. To maintain the time efficiency of existing recognizers and make text images more recognizable for recognizers, we resize the input image into $32 \times 256$ for all experiments. We utilize the validation set of each dataset to choose the best hyper-parameters according to the recognition accuracy, then assess the baselines using the testing set. For convenience, we combine the alphabets of four datasets for all experiments, resulting in an overall alphabet of 7,934 characters. No other strategies like data augmentation and pre-training are used for the baselines.

\textbf{Evaluation protocols.} Practically, the unified evaluation protocols are indispensable for fair comparison. Following the ICDAR2019 ReCTS Competition\footnote{https://rrc.cvc.uab.es/?ch=12\&com=tasks}, we leverage some rules to convert the predictions and labels:  (1) Convert full-width characters to half-width characters; (2) Convert traditional Chinese characters to simplified characters; (3) Convert uppercase letters to lowercase letters; (4) Remove all spaces. After these transformations, we utilize the widely-used metric Accuracy (\textbf{ACC}) to evaluate the baselines. In addition, Normalized Edit Distance (\textbf{NED}) is utilized to comprehensively evaluate the performance of baselines since CTR datasets contain more long text images compared with English text recognition datasets. ACC and NED are both ranged in [0,1]. Higher ACC and NED indicate better performance of the evaluated baseline.

\textbf{Experimental results.} We first analyze the experimental results (see Table \ref{tab:new baseline result}) of different methods. We observe that the performance of CRNN \cite{shi2016end} on each dataset is better than that of those vanilla attention-based recognizers (\textit{i.e.}, MORAN \cite{luo2019moran}, SEED \cite{qiao2020seed}, and SAR \cite{li2019show}), which are vulnerable to the attention drift problem \cite{cheng2017focusing} when encountering text images with longer Chinese texts. Although the performance of CRNN on the scene dataset is not as good as those transformer-based methods, CRNN has fewer parameters and costs less time in inference. Moreover, we notice that SEED does not perform well on all datasets. A possible reason may be that SEED needs to map each text image to its corresponding semantic embedding guided by fastText \cite{bojanowski2017enriching}, whereas Chinese text usually contains complex semantics, thus bringing difficulties to the semantics learning procedure. Obviously, TransOCR \cite{chen2021scene} surpasses all its counterparts as it is capable of modeling the sequential patterns more flexibly. Next, we analyze the experimental results from the perspective of dataset. As shown in Table \ref{tab:new baseline result}, all baselines do not perform well on the handwriting dataset due to the scribble in this dataset. As demonstrated in Table \ref{tab:visual experiment}, almost 40\% of the text samples in the handwriting dataset is marked as ``Scribble''. Practically, the writer may join up or omit some strokes to accelerate the writing speed, which indeed brings difficulties to the existing methods. On the contrary, the recognition accuracy of all baselines can exceed 90.0\% in the document dataset since the text samples in this dataset are more recognizable compared with the other three datasets. Although more samples are contained in the scene dataset, some problems (\textit{e.g.}, occlusion, background confusion, blur, etc.) still challenge the baseline methods, leading to the relatively poor performance on the scene dataset. For the web dataset, the performance of all baselines is lower than that of the scene dataset, which may stem from the scarcity of training samples.

\begin{table*}[t]
\small
\caption{The results of the baselines on hard cases. ACC / NED follows the percentage format and decimal format, respectively.
}
\centering
\scalebox{0.97}{\begin{tabular}{p{2.1cm}<{\centering} p{1.7cm}<{\centering} p{2.4cm}<{\centering} p{2cm}<{\centering} p{1.7cm}<{\centering} p{1.9cm}<{\centering}}
\toprule
Methods & Occlusion & \small{Oblique or Curved} & \small{BG Confusion} & Blur & Vertical Text \\
\midrule
CRNN \cite{shi2016end} & 22.0 / 0.531 & 13.0 / 0.389 & 22.0 / 0.621 & 34.0 / 0.535 & \; 2.0 / 0.164 \\
ASTER \cite{shi2018aster} & 28.0 / \textbf{0.641} & 21.0 / \textbf{0.510} & 32.0 / 0.710 & 39.6 / \textbf{0.658} & 21.0 / \textbf{0.491} \\
MORAN \cite{luo2019moran} & 21.0 / 0.459 & 10.0 / 0.280 & 17.0 / 0.479 & 31.8 / 0.441 & 10.0 / 0.166 \\
SAR \cite{li2019show} & 19.0 / 0.532 & 15.0 / 0.379 & 23.0 / 0.600 & 30.8 / 0.507 & \; 6.0 / 0.223 \\
SEED \cite{qiao2020seed} & 12.0 / 0.469 & \; 5.0 / 0.288 & 22.0 / 0.566 & 28.8 / 0.521 & \; 9.0 / 0.384 \\
MASTER \cite{lu2021master} & 26.0 / 0.509 & 12.0 / 0.314 & 30.0 / 0.587 & 41.2 / 0.532 & 13.0 / 0.199 \\
ABINet \cite{fang2021read} & 20.0 / 0.512 & 14.0 / 0.354 & 19.0 / 0.545 & 38.0 / 0.570 & \; 8.0 / 0.200 \\
TransOCR \cite{chen2021scene} & \textbf{32.0} / 0.581 & \textbf{29.0} / 0.503 & \textbf{44.0} / \textbf{0.741} & \textbf{50.4} / 0.626 & \textbf{32.0} / 0.440 \\
\bottomrule
\end{tabular}
}
\label{tab:hard case}
\end{table*}

\subsection{Discussions}

\textbf{Pretrained on synthetic datasets.} Considering that previous English text recognition methods tend to be trained on large-scale synthetic datasets, we also produce a synthetic CTR dataset to pretrain baseline models for further improving the performance. Details about the synthetic CTR dataset and experimental results are shown in the Supplementary Material. Through extensive experiments, we observe that pretraining baseline models with the synthetic dataset can indeed improve performance in most cases. Although a large synthetic dataset is used for pretraining, baseline models still cannot achieve the expected performance due to the characteristics of Chinese texts.

\textbf{Effectiveness of the PRAB.} Considering the characteristic of Chinese characters that each Chinese character can be decomposed into a specific radical sequence according to cjkvi-ids\footnote{https://github.com/cjkvi/cjkvi-ids}, we propose a Pluggable Radical-Aware Branch (PRAB) to introduce the more fine-grained supervision (\textit{i.e.}, radicals) for each character. We conduct extensive experiments to evaluate the effectiveness of PRAB by equipping it for all attention-based methods mentioned in Section \ref{baseline}. Through the experimental results (shown in the Supplementary Material), we observe that an attention-based recognizer can perform better on four datasets when the proposed PRAB is used to provide radical-level supervision, which validates the effectiveness of PRAB. Specifically, with the help of PRAB, the average accuracy improvement on MORAN, SAR, and TransOCR is 1.83\%, 3.35\%, and 2.33\% respectively.

\textbf{Analysis on hard cases.} We manually select some hard cases (\textit{e.g.}, occlusion, oblique or curved, confusing background, blur, and vertical) and analyze the performance of baselines on these cases\footnote{We manually pick 100 occlusion samples, 100 oblique or curved samples, 100 confusing background samples, 100 vertical samples, 500 blur samples in the scene and web datasets.} Please note that the ``Scribble'' situation is mainly associated with the handwriting dataset so we do not analyze the ``Scribble'' case separately. The experimental results on these hard cases are shown in Table \ref{tab:hard case}. In particular, we observe that TransOCR \cite{chen2021scene} can well adapt to each hard case compared with other baselines. Benefiting from the self-attention modules, the recognizer can easily tackle uncommon text layouts like oblique and curved, or mitigate the noise induced by confusing foregrounds (occlusion) or confusing backgrounds. Interestingly, ASTER\cite{shi2018aster} achieves better NED compared with TransOCR \cite{chen2021scene} in most situations as the STN utilized by ASTER increases the recognizability of those hard samples with a certain degree of oblique. Additionally, the CTC-based CRNN \cite{shi2016end} simply transforms original input to 1-D features, thus performing poorly on the vertical text images. Overall, there is still large room for improvement in terms of these hard cases.

\section{Conclusions}
In this paper, we first discuss possible reasons for the scarce attention on Chinese text recognition. To tackle these problems, we collect the publicly available datasets and divide them into scene, web, document, and handwriting datasets, respectively. We also analyze the characteristics of each dataset. Then we standardize the evaluation protocols in CTR (\textit{e.g.}, whether researchers should treat the traditional and simplified Chinese characters as the same character), which enables the researchers without Chinese character knowledge to participate in CTR research. Finally, we adopt eight representative methods as baselines on the collected datasets. Through the empirical study, we observe that combining the Chinese character knowledge is helpful for the Chinese text recognition task, which also provides guidance for future work of CTR.

{\small
\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


text:
Fiction /
Christian /
Historical /
A Rumored Fortune
By Joanna Davidson Politano
Tressa Harlowe's father did not trust banks, but neither did he trust his greedy extended family. He kept his vast fortune hidden somewhere on his estate in the south of England and died suddenly, without telling anyone where he had concealed it. Tressa and her ailing mother are left with a mansion and an immense vineyard and no money to run it. It doesn't take long for a bevy of opportunists to flock to the estate under the guise of offering condolences. Tressa knows what they're really up to. She'll have to work with the rough and rusticated vineyard manager to keep the laborers content without pay and discover the key to finding her father's fortune--before someone else finds it first.
Award-winning author Joanna Davidson Politano welcomes readers to Trevelyan Castle, home of the poorest heiress in Victorian England, for a treasure hunt they'll not soon forget.
Manufacturer: Fleming H Revell Co
Author: Joanna Davidson Politano

redpajama_set_name: RedPajamaCommonCrawl


So says photographer Ben Moon (quoting writer David Dudley) in this deeply moving tribute to Denali, his late dog. It suggests we can learn a lot from dogs, including the best way to greet the ones we love.
Watch this video. It's not only touching, it's incredibly important in how it reveals the beauty and power dogs have in our lives.


text:
Delaney Reyburn
LIFE ON THE GOLD COAST, AUSTRALIA
This travel blog has been a long time coming. It's something I've had in the back of my mind but have made excuses as to why I don't have the time/resources/creativity to just freakin' do it. But hey, as the saying goes "new year, new me" SO here goes... 5 months into life on the Gold Coast and its safe to say this officially feels like home. Well sort of... it feels like a continuous vacation but with laundry and work thrown into the mix. But who doesn't want to live life like its a permanent vacation? I think we got quite lucky with our living situation. Our apartment is small and nothing luxurious but you get that feeling of "ahh, its nice to be home," whenever we do walk in after being away. It sits on a golf course with the golf club kitty corner to us, but its nestled away from the main roads in a quiet neighborhood. We're just 10 minutes from shopping, beach, groceries and pretty much anything else we need. We're walking distance from our favorite cafe (Goji) where I have accumulated quite the impressive stamp card. Upon first arriving a few months ago, things did not start of smooth. I guess that should've been a given moving to the other side of the world with a last minute notice of where we would be placed within Australia, BUT I don't think we were quite prepared for the stress that does come with moving. We started off in a shit-hole hostel with all of our personal belongings thrown in a locker with a lock that could've been cut with hair trimmers. We couldn't get access to our American bank accounts because it was Labor Day weekend, we had no form of transportation, we had no phone or internet access ALL while trying to find an apartment, have Eric run his online business, and find our way around a completely new place we knew nothing about. There were tears shed, doubts about our decision and complete chaos while trying to figure out what the hell we were doing. At the time I remember saying, "months from now, we'll look back at all of this and laugh." Well, we're not really laughing (still too soon) but we are smiling ear to ear knowing we figured it out, all of it, completely on our own. We have a steady job, we travel often, we explore every inch of the G.C., we've made beautiful friends, we (sort of) have money, we have a car, and we are nicely settled in our homey apartment. GO US!!! We work at Bond University, a private university that sits on a beautiful lake just 10 minutes inland from the coast. Its a small Uni, but its super extra and is notorious for its dumb, rich kids. We're considered "hosts" but we really do everything in the food & beverage department. From serving food, cleaning tables, to bartending and working lavish events, we're basically placed wherever they need us. It's not something I'd want to do with my life, but Eric and I both agree we definitely got lucky with this one. Everyone we work with is lovely, so sweet and caring. We get time off whenever we need it and we get really decent hours with great pay. Definitely can't complain. We'll continue to work here through the beginning of March and then our Visa makes us find a different job. We're contemplating looking at something in a different area - possibly Sunshine Coast or even Melbourne! We'll have to do some research to figure out what is realistic.
WHERE THE SEA MEETS THE MOUNTAINS
I've been trying to figure out how I would sum up the Gold Coast to someone who has never been, and here's what I've got: Imagine a place that combines beautiful beaches, impressive hiking, tourism,
MY FIRST IMPRESSION OF INDONESIA
The Nusa Islands were an experience unlike anything I could have imagined. We spent four nights on the island, Nusa Lembongan. One of three islands off of the coast of Bali, and much more secluded tha
NEXT STOP: BALI
The next four nights in Indonesia were filled with adventures through rice terraces, lush forests, waterfalls and scooter excursions. Ubud is in the center of Bali. It's far from the coast but proudly
delaney.reyburn@gmail.com
©2020 by Delaney Reyburn. Proudly created with Wix.com


According to Professor Éric Troncy of the University of Montreal's Faculty of Veterinary Medicine, arthritis pain in dogs can be alleviated, with no side effects, by a combination of medicinal plants and dietary supplements.
To treat arthritis-related inflammation, the first formula contained curcumin, devil's claw, blackcurrant, Indian frankincense (Salai), willow bark, pineapple bromelain, and chamomile.
The second formula was intended to promote joint regeneration, and included the same ingredients as the first formula, plus omega-3 fatty acids, chondroitin sulfate, and glutamine.
The results were measured using three different methods: the dogs walking at a consistent speed on a special platform that recorded the strength of each paw; a special electronic collar that recorded the dogs' daily activities; and owner evaluations of their dog's behavior.
Troncy and his team were able to see improvement by the fourth week of the experiment, and by the end of week eight, "the strength of the dogs receiving treatment had improved to the equivalent of a kilo (2.2 pounds) of extra strength per paw," according to Maxim Moreau, lead author of the study.
Improvements were also seen in the dogs' daily lives. Those receiving the supplements maintained their physical activity levels, and the group average actually increased from six hours of activity to eight. The researchers were so surprised by these results that they videotaped some of the dogs to ensure their collars were recording actual physical activity and not simple movements like scratching.
Sadly, the dogs in the control group grew progressively less active over the eight week study.
Owner evaluations of their dog's behavior were necessarily subjective. Results didn't reveal a marked difference between the treated and control groups of dogs. The researchers suspect that some owners may have forgotten what their pet's behavior was like before developing arthritis.
While the formulas developed at the University of Montreal aren't commercially available, their study highlights the potential of nutraceuticals in veterinary medicine. I believe the more studies like this that are completed, the more tools we will have to treat painful and debilitating conditions, such as arthritis, naturally.
There are many other natural substances and therapies that have been shown to be beneficial for pets with arthritis.
Chiropractic care. Proper alignment prevents your dog's body from shifting into unhealthy positions to compensate for an injured or painful area, which can create problems down the road.
Massage. Massage is an excellent way to treat tissue inflammation and prevent secondary compensation in your dog's body.
Stretching. Stretching your dog can reduce degeneration and prevent soft tissue injury.
Acupuncture. Acupuncture treatments can be very beneficial for some dogs with degenerative joint disease.
There are several types of physical therapy that can benefit arthritic dogs. For example, gentle hydrotherapy in a pool or on an underwater treadmill can build and maintain muscle strength and endurance with minimum stress to painful joints. Also helpful are therapies that focus on coordination, flexibility, and balance.
Cryotherapy (cold packs) and heat therapy, extracorporeal shockwave therapy (ESWT), and low-level laser therapy can also be extremely beneficial in keeping an arthritic pet comfortable and mobile.
Chondroprotective agents (CPAs), which protect the joints, are a must for any dog with osteoarthritis. The type, form, and dose of CPA your veterinarian prescribes will be based on your dog's individual situation. Since each animal responds differently to CPAs, sometimes it's necessary to try a variety of products to find the ones most beneficial for a pet's specific symptoms.
Unfortunately, arthritis is a progressive disease, so it's important to routinely monitor your pet's symptoms and adjust her arthritis protocol to meet the changing demands of her body.


That is actually a great DIY! Very innovative! It sure beats putting repeaters,extenders or even setting up a complex Wireless distribution system. Do you have an idea as to how much the range was extended in terms of percentage?
See how far your WiFi currently extends from the router. Say it's 6 metres. Now run the coaxial cable. If the coaxial cable is 15 metres long, and you put an antenna on the end, you should now have WiFi 21 metres away from the router. However it's not really 'extended' because you may also have created a 'hole' in the WiFi coverage at around 6-9 metres (where one antenna ends and before the other picks up). So your mileage may vary.
I also found it helped to locate the antenna high up the wall, otherwise presumably you are losing some reception under the ground?


We want to thank Lindsay Bradford for beginning her Life Chest journey! We are honored to be a part of it!
"So excited! I have officially started my The Life Chest journey. So far I have included the three military spouse magazines from MSOY events, challenge coins, the cards from my amazing Branch Spouses and fellow MSOYs, sea air and space name card which was a special event for me, and my ticket from the holocaust museum.
Can't wait to keep adding.
(thank you Donna, I received the mug!)"


Ryan Brown, conductor and artistic director of Opera Lafayette and historian, Judith A. Miller, Associate Professor of History, Emory University, explore the operatic Greek tragedies of Sapho, Médée, and Œdipe à Colone performed during the French Revolution. With the French Revolution raging in the streets, how did the artistic milieu of Paris react?
This lecture is based on Opera Lafayette's full length production at Lincoln Center's Rose Theater. While musicologists have written extensively about music and the French Revolution in the last decade, performances of many of these works have yet to be heard in New York. Martini's Sapho will be a modern premiere. The Lecture looks at three dramatic scenes from classical Greek tragedies set by Martini, Cherubini and Sacchini. These operas held the stage for multiple performances in the 1790s and reflect the tumultuous times of the French Revolution. They hope to shed light on how music expresses feelings about important societal events that may not be able to expressed in the course of daily life, and how this is important not only for creators but for audiences.
Ryan Brown is a recipient of La Medaille d'Or du Rayonnement Culturel from La Renaissance Française.
Future plans for this project include a fully staged presentation of the program with soloists, orchestra and chorus at Lisner auditorium at George Washington University on April 29th and the upcoming Rose Theater at Lincoln Center concert on May 1 mentioned above. The lecture will feature music from their production performed by a soprano, Raquel Gonzalez and pianist Kathy Detwiler from the Opera Lafayette Company.
Soprano Raquel González from Lawrence, Kansas is in her second season at Washington National Opera's Domingo-Cafritz Young Artist Program, where she sings Micaëla in the Emerging Artist Performance of Carmen and The Sandman in Hansel and Gretel. She then makes her role debut as Desdemona in Otello with the Boston Youth Symphony Orchestra and returns to The Glimmerglass Festival as Mimì in La Bohème. In the 2014-2015 season she performed the role of Mimì in the WNO Emerging Artist performance of La Bohème. She also appeared as Sister Claire and covered Blanche de la Force in Dialogues of the Carmelites. As a 2015 Young Artist at The Glimmerglass Festival, Ms. González sang First Lady in The Magic Flute. As a Gerdine Young Artist at Opera Theatre of Saint Louis, she appeared in the world premiere of Terence Blanchard's Champion and returned to OTSL in the summer of 2014 as a Festival Artist singing First Lady (The Magic Flute) and covering Blanche de la Force (Dialogues of the Carmelites).At The Juilliard School Ms. González was seen as Tatiana (Eugene Onegin), Arminda (La finta giardiniera), Dido (Dido and Aeneas), and Mélisse (Armide). She spent several summers at the Chautauqua Institution singing such roles as Magda (La Rondine), Donna Anna (Don Giovanni), Adina (L'elisir d'amore), and Lauretta (Gianni Schicchi).In concert, she has appeared with the New York Festival of Song at Alice Tully Hall where she also appeared several times with William Christie and Juilliard415. Ms. González was the soprano soloist in Handel's Messiah with the Charleston Symphony Orchestra and made her debut with the New York Philharmonic as soprano soloist in Beethoven's Ninth Symphony at Avery Fisher Hall. A 2014 Eastern Region Finalist of the Metropolitan Opera National Council Auditions, she has also been awarded prizes from the Liederkranz Foundation and the Gerda Lissner Foundation. She earned her master's degree as a Toulmin Scholar from The Juilliard School, where she also completed her undergraduate studies.


text:
Elharo (talk | contribs)
m (→If I'm careful with the syntax I use in my HTML document, can I process it with an XML parser?: serialisation --> serialization, as per spelling used in HTML 5 spec)
Ms2ger (talk | contribs)
m (→HTML5: Note references)
=== The references links are broken! ===
Indeed, the references section hasn't been filled in yet. This should happen around Last Call. For now, you can find (some of) the references [[HTML5 References|on this wiki]].
== Syntax issues ==
3.6 The references links are broken!
4.6 What is an HTML Serialization?
4.7 What is an XML (or XHTML) Serialization?
5.6 The <cite> element should allow names of people to be marked up
5.7 The <time> element should allow vague times ("March") and times from ancient history to be marked up
The references links are broken!
Indeed, the references section hasn't been filled in yet. This should happen around Last Call. For now, you can find (some of) the references on this wiki.
What is an HTML Serialization?
The HTML serialization refers to the syntax of an HTML document defined in HTML5. The syntax is inspired by the SGML syntax from earlier versions of HTML, bits of XML (e.g. allowing a trailing slash on void elements, xmlns attributes), and reality of deployed content on the Web.
What is an XML (or XHTML) Serialization?
The HTML serialization must be served using the text/html MIME type.
The XHTML serialization must be served using an XML MIME type, such as application/xhtml+xml or application/xml. Unlike XHTML 1.0, XHTML 5 must not be served as text/html.
No, HTML and XML have many significant differences, particularly parsing requirements, and you cannot process one using tools designed for the other. However, since HTML5 is defined in terms of the DOM, in most cases there are both HTML and XHTML serializations available that can represent the same document. There are, however, a few differences explained later that make it impossible to represent some HTML documents accurately as XHTML and vice versa.
In XHTML, you are required to specify the namespace.
In addition, the HTML syntax provides for a way to embed elements from MathML and SVG. Elements placed inside the container element math or svg will automatically be put in the MathML namespace or the SVG namespace, respectively, by the parser. Namespace syntax is not required, but again an xmlns attribute is allowed if its value is the right namespace.
In conclusion, while HTML5 does not allow the XML namespace syntax, there is a way to embed MathML and SVG and the xmlns attribute can be used on any element under the given constraints, in a way that is reasonably compatible on the DOM level.
Authors can use the microdata feature (the item="" and itemprop="" attributes) to embed nested name-value pairs of data to be shared with other applications and sites.
There is currently no mechanism for introducing new proprietary features in HTML documents (i.e. for introducing new elements and attributes) without discussing the extension with user agent vendors and the wider Web community. This is intentional; we don't want user agents inventing their own proprietary elements and attributes like in the "bad old days" without working with interested parties to make sure their feature is well designed.
The inclusion of these elements is a largely pragmatic decision based upon their widespread usage, and their usefulness for use cases which are not covered by more specific elements.
The <cite> element should allow names of people to be marked up
From what we've seen, <cite> is almost always used to mean "italics". More careful authors have used the element to mark up names and titles, and some people have gone out of their way to only mark up citations.
So, we can't really decide what the element should be based on past practice, like we usually do.
This leaves the question of what is the most useful use we can put the element to, if we keep it. The conclusion so far has been that the most useful use for <cite> is as an element to allow typographic control over titles, since those are often made italics, and that semantic is roughly close to what it meant in previous versions, and happens to match at least one of the common uses for the element. Generally, however, names and titles aren't typeset the same way, so making the element apply to both would lead to confusing typography.
There are already many ways of marking up names already (e.g. the hCard Microformat, the microdata vCard vocabulary, <span> and class names, etc), if you really need it.
The <time> element should allow vague times ("March") and times from ancient history to be marked up
This has been discussed a number of times. For an overview of the topic, please see these e-mails:
http://lists.whatwg.org/htdig.cgi/whatwg-whatwg.org/2009-March/018888.html
http://lists.whatwg.org/htdig.cgi/whatwg-whatwg.org/2009-August/021745.html
At this stage, as discussed in the second of those e-mails, the best way forward is to demonstrate that there are communities interested in solving this problem, by using existing techniques such as microdata to address it. If such a solution achieves a high adoption rate, that will substantially increase the strength of the proposals.


Contact Juliusz for session rate.
In my view, the key to any change resulting from counselling and psychotherapy is from the trust that exists between the client and the therapist and which emanates from the relationship. With that in mind, although my training is integrative (meaning that I have trained in different modes and approaches to therapy) the work that I do, and my approach, is focused on the client's needs.


text:
'Earwig And The Witch' Director Gorô Miyazaki On The Challenging Process Of Crafting Studio Ghibli's First 3D CG Feature
Alto Reed Dies: Bob Seger's Longtime Sax Player Was 72
By Erik Pedersen
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Robert E. Klein/AP
Alto Reed, who played saxophone for multiplatinum Bob Seger's Silver Bullet Band for nearly half a century, died Wednesday of colon cancer. He was 72.
Seger posted a note about his "lifelong friend and bandmate" on social media: "He was amazing – he could play just about anything," he wrote. "In our band, he was the rock star." Read the full post below.
Bob Seger and the Silver Bullet Band, circa 1978, from left: Drew Abbott, Robyn Robbins, Alto Reed, Bob Seger, Chris Campbell and Charlie Allen Martin
Born Thomas Cartmell in Detroit in 1948, Reed was known for his showmanship onstage and his signature sax riff on "Turn the Page." He first started playing with Seger in the early 1970s, played on its Back in '72 albums and joined the band for its first headlining arena shows at Detroit's Cobo Hall. By 1974, he was a full-fledged member of the Silver Bullet Band, which was about to break nationally with "Live" Bullet (1976), which was recorded at Cobo Hall and featured a scorching version of the Reed-fueled "Turn the Page" that remains a staple on classic rock radio. Metallica covered the song on its 1998 album Garage Inc., with a guitar riff subbing for the sax.
Notable Hollywood & Entertainment Industry Deaths In 2020: Photo Gallery
Reed joined Seger and several other longtime bandmates for a 2018-19 farewell tour, during which he played the familiar guitar riff from "Mainstreet" on sax. His horn also helped drive such Seger classics as "Rock and Roll Never Forgets" and the 1978 hit "Old Time Rock and Roll."
He was part of the band during its 1970s and '80s heyday, playing on the multiplatinum albums Beautiful Loser, Night Moves, Stranger in Town, Against the Wind — which was No. 1 on the Billboard 200 for six weeks in 1980 — and the 1981 live set Nine Tonight. The single "Against the Wind" won the group it only Grammy, for Best Rock Performance by a Duo or Group, and also scored a second nom that year.
The group scored eight consecutive Top 10 discs from 1976-94, including Greatest Hits, which has sold more than 10 million units in the U.S. alone. The band's The Distance, Like a Rock, The Fire Inside, Face the Promise and Greatest Hits 2 also sold at least 1 million units, and 1996's It's a Mystery went gold.
Bob Seger and the Silver Bullet Band also racked up seven Top 10 singles, ranging from the 1976 classic "Night Moves" to the group's lone chart-topper, "Shakedown," from the 1987 film Beverly Hills Cop II. The title cut from Like a Rock peaked at No. 12 in 1986 but became nearly as familiar as any of the group's songs as the featured cut in a long-running TV ad campaign for Chevrolet trucks during the 1990s and early 2000s.
Seger was inducted into the Rock and Roll Hall of Fame in 2004, but his Silver Bullet Band was not recognized.
Along with his Seger work, Reed performed with such acts as Little Feat, Foghat, Dave Mason, Spencer Davis, Grand Funk Railroad, Ted Nugent, the Blues Brothers, George Thorogood and the Motor City Horns. He also co-fronted the Reed & Dickinson Band with Steve Dickinson.
Reed is survived by daughters Chelsea Reed Radler and Victoria Reed and their spouses; his partner, Christiana Van Ryn; her daughter, Sophia Van Ryn; and sister Nancu Neumann. In lieu of flowers, Reed's family requested that donations in his name be made to Detroit Symphony's Detroit Harmony Fund.
Alto Reed
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Storage Units Bristol, CT
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Our storage units range from $20.00 to $637.00 per month at our locations near Bristol. Be sure to check each store in the nearby areas for the rental prices we offer. The prices can vary from store to store and you may find a great price not too far away. Our minimum rental is for one month. These spaces range in size from 15 sq. ft. to 1000 sq. ft., and come in a variety of climate and accessibility types. Look for a great move-in offer! Our spaces often have a special discount for new customers. Spaces with a special offer will be marked with the discount you'll receive. Hamden, CT starting at $20 per month: starting at $20 per mo. Glastonbury, CT starting at $32 per month: starting at $32 per mo. Suffield, CT starting at $41 per month: starting at $41 per mo. North Haven, CT starting at $45 per month: starting at $45 per mo. Waterbury, CT starting at $48 per month: starting at $48 per mo. Wallingford, CT starting at $52 per month: starting at $52 per mo.


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State Chapter Staff
Support a DFER
Featured DFERs
A Democratic Guide to Public Charter Schools: Charter School Outcomes
Today we present the fifth and final installment of the 2nd Edition of the Democratic Guide to Public Charter Schools. And we saved the best for last: The reason we support public charter schools is because they work for those students whom they are intended to serve. Across the entire country, public charter schools have given low-income students and children of color an alternative to the chronically low-performing traditional public schools to which they would have otherwise been assigned.
The most rigorous and methodologically sound studies indicate that students of color, students in poverty, and English Language Learners (ELLs), enrolled in public charter schools make significantly greater academic progress compared to their peers with similar demographics in traditional public schools. Our best urban public charter schools provide students with the equivalent of three to five months of additional learning time in math and reading as compared to what they would have received otherwise.
Read the full section here.
Also see: Part 1, Origins, Part 2: Support from Democratic Leadership, Part 3: Public Opinion Research, and Part 4: Charter Enrollment in Democratic Strongholds.
Democratic Guide to Public Charter Schools, 2nd Edition
Pod Save the People w/ DFER National President Shavar Jeffries
Albany Times-Union: Education Reform Group Says School Turnaround Program Has Been "Hollowed Out"
Teacher Effectiveness & Evaluations
Teachers Unions
© 2020 Democrats for Education Reform. All Rights Reserved.
Paid for by Democrats for Education Reform, 840 First Street NE, 3rd Floor, Washington, DC 20002. Contributions are not tax deductible


I dont know if this has been posted yet but Linux 2.20j - b43 p3 server is running tests for ET. The ip is 192.246.40.70:27960 and its a locked server big surprise. But you all can see the different map names and different people playing on the server and u may be able to squeeze info out of them about ET.


The Taiwanese city of Taoyuan is investigating charges that an important Apple supplier, Career Technology, pushed 200 full-time workers to sign "voluntary" resignations.
Workers said operations at a Taoyuan factory have been shut down for over two weeks, and that the resignations were forced so Career could avoid paying severance, according to The China Post. On Jan. 29 and 31 the company reported laying off just 36 and 23 people, respectively.
In its defense Career claimed that there was a misunderstanding, and workers were asked to sign a form with three options: resignation, retirement, or layoff. It did admit to having to shrink its workforce at the factory, but said 20 to 30 people were impacted, either in layoffs or transfers to a factory in New Taipei — in conflict with its earlier official statistics. It also promised to pay legally required benefits and severance.
Taoyuan Department of Labor staff will visit Career when it reopens after the Lunar New Year holiday.
Career produces flexible circuit boards for iPhone antennas, and in November laid off 400 to 500 temporary workers as a result of weak orders from Apple. If the new allegations are accurate it may suggest the company was deeply impacted, given that full-time staff are usually more secure at Apple suppliers.
The company's downturn in iPhone sales during the December quarter has impacted other companies in the Apple supply chain, among them Foxconn, which reportedly dismissed some 50,000 seasonal workers early. The company typically ramps up hiring ahead of fall iPhones launches and keeps seasonal crews onboard until January.


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NGO Working Group on Women, Peace and Security
Acknowledgements and Credits
Monitoring & Analysis
Peacebuilders
Mapping Women, Peace and Security in the UN Security Council: 2018
This policy brief outlines the findings from the NGO Working Group on Women, Peace and Security's (NGOWG) monitoring and analysis of the United Nations (UN) Security Council's daily work over the course of 2018.
To view the full report, please download the PDF.
The overall aim of the policy brief is to assess the implementation of the women, peace, and security (WPS) policy framework in the work of the Security Council (SC). The analysis and recommendations build on our well-established policy guidance project, the Monthly Action Points (MAP) on Women, Peace and Security, as well as broader advocacy throughout 2018.
A year before the 20th anniversary of the adoption of United Nations (UN) Security Council Resolution 1325 (2000), progress on women's rights has wavered both outside and within the UN. Despite a strong normative framework, repeated commitments by the UN system and Member States, and the wealth of evidence on the importance of ensuring gender equality and women's participation in peace and security efforts, implementation of the agenda continues to be uneven and selective, and normative progress a challenge.
Our analysis of the Security Council's performance over 2018 shows that its approach to WPS remains superficial, ad-hoc and inconsistent; subject to the individual efforts of Security Council members rather than being systematically integrated into Council action; and reflects a lack of willingness to tackle the harder and more complex issues under the WPS agenda. Its approach is defined by a lack of accountability for failure to fully implement all provisions of the ten resolutions that constitute the WPS agenda.
Conflicts around the world disproportionately impact the health, safety, and the rights of women, yet women continue to be excluded from decision-making processes that determine their future. Although the Security Council paid increased attention to women's participation in peace processes in 2018, women still faced challenges at every level, and at every stage, of peace and reconciliation processes and dialogues currently taking place. As has been widely noted, the Yemen peace talks held in Sweden last December included one woman out of the government's 12 delegates, while the Houthi delegation included none. The resulting Stockholm Agreement, which does not contain any specific provisions on gender or women, fails to adequately address the deeply gendered impact of the conflict on Yemeni women and girls. Although there were attempts to include Yemeni women in other ways, such as the role of various advisory groups at different stages of the process, exclusion from the formal process led to a lack of meaningful participation and limited influence over the outcome.1 Similarly, peace talks between the United States and the Taliban sidelined not only the Afghan government, but also Afghan women, whose rights are at particular risk. There are many other examples of women's exclusion from peace processes or where women's rights have been viewed as dispensable or secondary to getting warring parties to the table.
Given the overwhelming evidence that gender equality is critical for conflict prevention, such exclusion actively undermines peace. This means that there needs to be more focused attention on the gendered challenges and barriers to participation, rather than mere rhetorical acknowledgment of the importance of women's participation. It also reflects the need for more attention to the full scope of women's rights. Our analysis shows that while attention to the important issue of sexual and gender-based violence (SGBV) continues to receive dedicated and annually increasing attention by the Security Council – in 2018, 90% of the references to women's rights in reports of peace operations were focused on SGBV, accounting for 45% of all references to WPS – women's rights more broadly have failed to receive an equal amount of attention. Recognition of the interrelated and mutually reinforcing nature of all pillars of the agenda – for instance, that protection of women from gender-based violence is inseparable from their meaningful participation, bodily autonomy and rights, and that ensuring accountability for violations of fundamental human rights is necessary in order to prevent relapse into conflict – remains critical for the advancement of the agenda as a whole.
The pushback against women's participation is not restricted to exclusion from formal negotiations over peace agreements. The Special Rapporteur on human rights defenders reported worsening violence against women human rights defenders in March 2019, and highlighted how "the rise in misogynistic, sexist and homophobic speech by political leaders in recent years has normalized violence against women human rights defenders."2 Meanwhile, new research published by the Armed Conflict Location & Event Data Project (ACLED) tracking active repression of women engaged in various ways in the public sphere, including in political processes and protests, shows that women have been facing "unprecedented levels" of violence, especially in the past 18 months.3 These global trends highlight how women routinely face retaliation for engaging in politics, for peacefully protesting, and for advocating for human rights, including their own. As long as ordinary women and LGBTIQ+ persons, human rights defenders (HRDs), women activists, and women politicians are the targets of violence and harassment, they cannot freely participate in public life. The failure of the Security Council to adequately address the issue of women HRDs in outcome documents is one of the clearest gaps in its implementation of the WPS agenda.
Normative progress on WPS has been arrested by growing geopolitical divides within the UN Security Council and broader attacks on multilateralism, as well as attempts from various quarters to undermine international human rights and humanitarian law. These attempts are taking place against the backdrop of the spread of populism, violent extremism, and growing economic inequality, all of which are deeply gendered phenomena.4 In the last year, there have been increasing and direct attacks on women's rights as well as on core principles of international humanitarian and human rights law, including as they apply to sexual and reproductive rights, and sexual orientation, gender identity, and expression, and sex characteristics (SOGIESC). These attempts have been made by a variety of actors around the world as well as within the very UN bodies tasked with protecting human rights and ensuring peace and security, such as the Security Council, the General Assembly and the Commission on the Status of Women.
The Security Council's continuing omission of the ways in which LGBTIQ+ persons are affected by conflict was starkly represented by the fact that there was only one reference to LGBTIQ+ persons in any Council document in 2018, and it was in a report of the Secretary-General. Similarly, only one reference was made to sexual and reproductive rights, also in the context of a report of the Secretary-General. Overall no outcome document adopted by the Security Council included any reference to these two crucial issues. However, one of the most concerning developments in the past 18 months was the context in which we saw the adoption of the ninth WPS resolution. Resolution 2467 (2019), passed under the German presidency of the Security Council in April 2019, proved to be an indicator of the worrying landscape for women's rights, which was also reflected in the difficult negotiations preceding the adoption. The threat of the veto by the United States led to the removal of explicit references to sexual and reproductive health in the context of a "survivor-centered approach" to conflict-related sexual violence, excluding one of the critical elements that survivors most need. Two permanent members of the Security Council, China, and Russia, after attempting to expunge human rights language and references to civil society, abstained for the first time on a WPS resolution. Divides within and outside the Council meant that language not only on sexual and reproductive health and rights but on other issues – such as HRDs, the International Criminal Court, and arms embargoes – also suffered. Although modest gains were made in several areas, the political cost of adopting Resolution 2467 (2019) was the shattering of consensus on the WPS agenda a year before the 20th anniversary of the adoption of Resolution 1325 (2000). Although the recently adopted Resolution 2493 (2019) raised concerns that it set a less than ambitious tone for the 19th anniversary of the WPS agenda, it affirmed Security Council consensus and cross-regional support around the existing normative framework.
The developments over the past year reinforce the need for the WPS agenda to be firmly grounded in gender equality and human rights, for both leadership and accountability by the UN system, and for Member States to recognize women's rights as essential for prevention of conflict and to take bold, principled action to protect and advance the normative framework of the WPS agenda. It is critical to recognize the interrelated, inseparable, and mutually reinforcing nature of all elements of the WPS agenda and the importance of full, not selective, implementation. A lack of accountability for failure to implement WPS both within the UN as well as outside it indicates that there is little to stop women's rights from being eroded, demoted or traded away in matters of peace and security. There must be greater institutional accountability and responsibility of senior UN officials for WPS to ensure that the UN consistently upholds these standards. Now, more than ever, it is also critical that Member States speak out when the rights of women and girls are under attack.
Defending the fundamental tenets of the agenda, including the full scope of women's human rights, and pushing for full implementation, while important, must not compromise advancing the agenda. Advances, however, require addressing identified gaps and ensuring a measurable impact on the ground. The most credible benchmark of meaningful advancement is a positive change in the lives of conflict-affected communities.
[i] Nassar, Yemen's Women Confront War's Marginalization, MERIP, 2018. https://merip.org/2019/03/yemens-women-confront-wars-marginalization/
[ii] OHCHR, Women human rights defenders face worsening violence, warns UN human rights expert, 28 February 2019. https://www.ohchr.org/EN/NewsEvents/Pages/DisplayNews.aspx?NewsID=24232&LangID=E
[iii] ACLED, 'Terribly and Terrifyingly Normal': Political violence targeting women, 2019. https://www.acleddata.com/2019/05/29/terribly-and-terrifyingly-normal-political-violence-targeting-women/
[iv] UN & World Bank, Pathways for Peace: Inclusive Approaches to Preventing Violent Conflict, 2018. https://openknowledge.worldbank.org/handle/10986/28337
Children & Armed Conflict Civil Society Operational Efforts in Areas of Conflict Conflict Prevention & Resolution Countering Violent Extremism & Terrorism Crime & Trafficking Dedicated Gender-Sensitive Resources Disarmament & Nonproliferation Disarmament, Demobilization & Reintegration Displacement Economic & Social Autonomy Exploitation of Girls Gendered Security Measures Health Human Rights Humanitarian Assistance Inclusive Decision-Making Structures/Institutions Initiatives to Derail Radicalization Involuntary Trafficking of Women ISIL (Da'esh) and Al-Qaeda Justice & Rule of Law Mediation & Peace Agreements Medical and Psychological Care for Survivors Migrant Smuggling Mine Action National Action Plans Participation of Women in Formal Peace Processes Piracy Post-Conflict Peace Building Protection of Civilians Refugee/IDP Conditions for Women & Girls Sanctions Security Sector Reform Sexual & Gender-Based Violence Sexual Orientation & Gender Identity Small Arms & Light Weapons Social, Racial & Gender Prejudice Strengthening the WPS Agenda Globally Sustainable Peace & Development Women Human Rights Defenders Women in Field-Based Peacekeeping Women, Peace & Security Agenda Women's Equality & Empowerment WPS Implementation in Policy & Practice Youth & Children
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Suge Knight Was NOT Arrested For Killing Tupac
Another day, another internet rumor. This time it involves the late great rap artist Tupac Shakur and Suge…
Another day, another internet rumor. This time it involves the late great rap artist Tupac Shakur and Suge Knight.
The internet never ceases to amaze me. I was on Facebook a few hours ago and saw all of these posts about Suge Knight supposedly being arrested in connection with Tupac's murder which happened a little over 14 years ago. And at first glance, it could've been true…well with Suge's history and all it didn't seem far fetched. But then I turned on the news and nothing's being reported on any of the news channels. There are literally no stories on TV to verify what I'm reading on Facebook and Twitter.
Now, I am not a rocket scientist, but if Suge Knight was really arrested for Tupac's murder, I'm positive that some news station would've covered it. There at least would've been a little news ticker or something scrolling along the bottom of the screen in fine print or something.
This is a video that supposedly breaks the news of this development and got the internet in a frenzy. This video is obviously a fake and if you listen to the very beginning of the video the guy says "11 years" since Tupac's murder which would mean that this news clip came out around the year 2007. And then at around the 1:53 mark in the vid, it clearly says that today's date is May 20th when obviously it isn't.
Yes, Suge Knight was arrested on Wednesday in California but according to CNN, he was arrested for failing to appear in court for traffic violations and that it had nothing to do with the infamous cold case.
Who killed Tupac has remained a mystery and no one has been proven to be the killer, but there were reportedly ties between Suge Knight, his record label and the killing of Tupac which obviously led to me even having to write this story to clarify that as of today, it's just a rumor. Nothing has been proven.
The moral to this story is to not fly off the handle just because you hear some news that you want to hear, and do proper research before making bold and silly accusations.
May Tupac R.I.P.
internet rumor
suge knight arrested for Tupac's murder
Brennen Jones
Brennen Jones is the leading founder and publisher of TheUrbanTwist.com, a leading entertainment and media blog. He is also the founder of P.S. Web Pros, which is a web design and internet marketing company. He's also TheUrbanTwist.com's resident filmmaker and has produced and directed several short films under his Kitt HoRizon Films imprint and he's produced and have worked on video productions and commercials for various government agencies. He plans to turn The Urban Twist into a media haven so stay tuned. Brennen currently lives in Baltimore, MD. So be my friend on Facebook and Follow me on Twitter!
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Will Janet Jackson's REAL First Child Make An Appearance Soon? Will We Ever Know The Truth About Her Alleged Child With James DeBarge?
Janet Jackson's child with first husband, James DeBarge, is said to be one of the music industries worst kept secrets. James has now added fuel the rumors in a new interview with RadarOnline. The singer claims to have recently spoken with the daughter Janet allegedly gave away behind his back.
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Lana Del Rey Gets Backlash For Tone-Deaf Comments On Her New Album Cover
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Chris Brown Rejects Plea Deal in Washington, D.C. Assault Case
Chris Brown rejects plea deal in Washington assault case because "he's not guilty", Lawyer says.