Offline handwriting recognition systems require cropped text line images for both training and recognition. On the one hand, the annotation of position and transcript at line level is costly to obtain. On the other hand, automatic line segmentation algorithms are prone to errors, compromising the subsequent recognition. In this paper, we propose a modification of the popular and efficient multi-dimensional long short-term memory recurrent neural networks (MDLSTM-RNNs) to enable end-to-end processing of handwritten paragraphs. More particularly, we replace the collapse layer transforming the two-dimensional representation into a sequence of predictions by a recurrent version which can recognize one line at a time. In the proposed model, a neural network performs a kind of implicit line segmentation by computing attention weights on the image representation. The experiments on paragraphs of Rimes and IAM database yield results that are competitive with those of networks trained at line level, and constitute a significant step towards end-to-end transcription of full documents.
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@cite_42: This paper presents a text line segmentation method for printed or handwritten historical Arabic documents. Documents are first classified into 2 classes using a K-means scheme. These classes correspond to document complexity (easy or not easy to segment). Then, a document which includes overlapping and touching characters, is divided into vertical strips. The extracted text blocks obtained by horizontal projection are classified into three categories: small, average and large text blocks. After segmenting the large text blocks, the lines are obtained by matching adjacent blocks within two successive strips using spatial relationship. The document without overlapping or touching characters is segmented by making abstraction on the segmentation module of the large text blocks. The text line segmentation method has a 96 accuracy on a collection of 100 historical documents
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@cite_27: In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.
@cite_45: We introduce quantization feature functions to represent continuous or large range discrete data into the symbolic CRF data representation. We show that doing this convertion in a simple way allows the CRF to automaticaly select discriminative features to achieve best performance. This system is evaluated on a segmentation task of degraded newspapers archives. The results obtained show the ability of the CRF model to deal with numerical features similarly as for symbolic representation thanks to the use of quantization feature functions. The segmentation task is achieved by the definition of a horizontal CRF model dedicated to pixel labelling.
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@cite_5: Abstract This paper proposes neural network-based text locations in complex color images. Texture information extracted on several color bands using neural networks is combined and corresponding text location algorithms are then developed. Text extraction filters can be automatically constructed using neural networks. Comparisons with other text location methods are presented; indicating that the proposed system has a better accuracy.
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@cite_11: In this paper we present an approach for text line analysis and detection in handwritten documents based on Hidden Markov Models, a technique widely used in other handwritten and speech recognition tasks. It is shown that text line analysis and detection can be solved using a more formal methodology in contraposition to most of the proposed heuristic approaches found in the literature. Our approach not only provides the best position coordinates for each of the vertical page regions but also labels them, in this manner surpassing the traditional heuristic methods. In our experiments we demonstrate the performance of the approach (both in line analysis and detection) and study the impact of increasingly constrained "vertical layout language models" on text line detection accuracy. Through this experimentation we also show the improvement in quality of the baselines yielded by our approach in comparison with a state-of-the-art heuristic method based on vertical projection profiles.
Classical text line segmentation algorithms are mostly based on image processing techniques and heuristics @cite_27 @cite_22 @cite_43 @cite_2 @cite_23 @cite_10 @cite_42 . However, some methods were devised using statistical models and machine learing techniques such as hidden Markov models @cite_11 , conditional random fields @cite_45 , or neural networks @cite_8 @cite_5 @cite_48 @cite_33 . In our model, the line segmentation is performed implicitely and integrated in the neural network. The intermediate features are shared by the transcription and the segmentation models, and they are jointly trained to minimize the transcription error.