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:
We address the problem of segmenting and retrieving word images in collections of historical manuscripts given a text query. This is commonly referred to as "word spotting". To this end, we first p ...

Cited Abstract(s):
[1]: Word spotting has become a field of strong research interest in document image analysis over the last years. Recently, AttributeSVMs were proposed which predict a binary attribute representation ( in IEEE Trans Pattern Anal Mach Intell 36(12):2552---2566, 2014). At their time, this influential method defined the state of the art in segmentation-based word spotting. In this work, we present an approach for learning attribute representations with convolutional neural networks(CNNs). By taking a probabilistic perspective on training CNNs, we derive two different loss functions for binary and real-valued word string embeddings. In addition, we propose two different CNN architectures, specifically designed for word spotting. These architectures are able to be trained in an end-to-end fashion. In a number of experiments, we investigate the influence of different word string embeddings and optimization strategies. We show our attribute CNNs to achieve state-of-the-art results for segmentation-based word spotting on a large variety of data sets.

[2]: We propose a deep convolutional feature representation that achieves superior performance for word spotting and recognition for handwritten images. We focus on: -(i) enhancing the discriminative ability of the convolutional features using a reduced feature representation that can scale to large datasets, and (ii) enabling query-by-string by learning a common subspace for image and text using the embedded attribute framework. We present our results on popular datasets such as the IAM corpus and historical document collections from the Bentham and George Washington pages. On the challenging IAM dataset, we achieve a state of the art mAP of 91.58 on word spotting using textual queries and a mean word error rate of 6.69 for the word recognition task.

[3]: 

[4]: In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision task such as classification, detection or segmentation. Due to their outstanding performance, CNNs are more and more used in the field of document image analysis as well. In this work, we present a CNN architecture that is trained with the recently proposed PHOC representation. We show empirically that our CNN architecture is able to outperform state of the art results for various word spotting benchmarks while exhibiting short training and test times.

[5]: In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representation that projects images and strings into a common attribute space based on a Pyramidal Histogram of Characters (PHOC). These attribute models are learned using linear SVMs over the Fisher Vector [8] representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi-gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets.

[6]: 

[7]: 

[8]: This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
Since then, many methods extending it have been proposed. In [5] [6] , the approach in [8] is extended to the segmentation-free setting. The method proposed in [2] replaces the Fisher Vectors by a convolutional neural network (CNN) and the PHOC representation is learned using SVMs. Two methods were simultaneously proposed where the two step Fisher Vector and CCA approach is replaced with end-to-end trainable CNNs [4] [7] . This strand of work has been consolidated and improved upon in [1] [3] .