You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The contribution of this paper is fourfold. The first contribution is a novel, generic method for automatic ground truth generation of camera-captured document images (books, magazines, articles, invoices, etc.). It enables us to build large-scale (i.e., millions of images) labeled camera-captured scanned documents datasets, without any human intervention. The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e.g., English, Russian, Arabic, Urdu, etc. To assess the effectiveness of the presented method, two different datasets in English and Russian are generated using the presented method. Evaluation of samples from the two datasets shows that 99:98 of the images were correctly labeled. The second contribution is a large dataset (called C3Wi) of camera-captured characters and words images, comprising 1 million word images (10 million character images), captured in a real camera-based acquisition. This dataset can be used for training as well as testing of character recognition systems on camera-captured documents. The third contribution is a novel method for the recognition of cameracaptured document images. The proposed method is based on Long Short-Term Memory and outperforms the state-of-the-art methods for camera based OCRs. As a fourth contribution, various benchmark tests are performed to uncover the behavior of commercial (ABBYY), open source (Tesseract), and the presented camera-based OCR using the presented C3Wi dataset. Evaluation results reveal that the existing OCRs, which already get very high accuracies on scanned documents, have limited performance on camera-captured document images; where ABBYY has an accuracy of 75 , Tesseract an accuracy of 50.22 , while the presented character recognition system has an accuracy of 95.10 .

Cited Abstract(s):
[1]: Most optical character recognition (OCR) systems need to be trained and tested on the symbols that are to be recognized. Therefore, ground truth data is needed. This data consists of character images together with their ASCII code. Among the approaches for generating ground truth of real world data, one promising technique is to use electronic version of the scanned documents. Using an alignment method, the character bounding boxes extracted from the electronic document are matched to the scanned image. Current alignment methods are not robust to different similarity transforms. They also need calibration to deal with non-linear local distortions introduced by the printing scanning process. In this paper we present a significant improvement over existing methods, allowing to skip the calibration step and having a more accurate alignment, under all similarity transforms. Our method finds a robust and pixel accurate scanner independent alignment of the scanned image with the electronic document, allowing the extraction of accurate ground truth character information. The accuracy of the alignment is demonstrated using documents from the UW3 dataset. The results show that the mean distance between the estimated and the ground truth character bounding box position is less than one pixel.

[2]: Abstract Geometric matching under uncertainty is a long-standing problem in computer vision. This paper presents a simple and efficient branch-and-bound algorithm for finding globally optimal solutions to geometric matching problems under a wide variety of allowable transformations (translations, isometries, equiform transformations, others) and a wide variety of allowable feature types (point features, oriented point features, line features, line segment features, etc.). The algorithm only requires an implementation of the forward transformation (model-to-image) and an error model to be supplied. Benchmarks and comparisons of the algorithm in comparison with alignment and Hough transform methods are presented.

[3]: In document image understanding, public datasets with ground-truth are an important part of scientific work. They are not only helpful for developing new methods, but also provide a way of comparing performance. Generating these datasets, however, is time consuming and cost-intensive work, requiring a lot of manual effort. In this paper we both propose a way to semi-automatically generate ground-truthed datasets for newspapers and provide a comprehensive dataset. The focus of this paper is layout analysis ground truth. The proposed two step approach consists of a module which automatically creates layouts and an image matching module which allows to map the ground truth information from the synthetic layout to the scanned version. In the first step, layouts are generated automatically from a news corpus. The output consists of a digital newspaper (PDF file) and an XML file containing geometric and logical layout information. In the second step, the PDF files are printed, scanned and aligned with the synthetic image obtained by rendering the PDF. Finally, the geometric and logical layout ground truth is mapped onto the scanned image.
Similarly, [1] proposed automatic ground truth generation for OCR using robust branch and bound search (RAST) [2] . First, global alignment is estimated between the scanned and ground truth images. Then, local alignment is used to adapt the transformation parameters by aligning clusters of nearby connected components. [3] proposed an approach for ground truth generation of newspaper documents. It is based on synthetic data generated using an automatic layout generation system. The data are printed, degenerated, and scanned. Again, RAST is used to compute the transformation to align the ground truth to the scanned image. The focus of this approach is to create ground truth information for layout analysis.