Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition

Bianne-Bernard, Anne-Laure; Menasri, F.; Mohamad, R. Al-Hajj; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

doi:10.1109/tpami.2011.22

Cited by 113 publications

(43 citation statements)

References 32 publications

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“…Several approaches have been proposed to build optical models for handwriting recognition. Such approaches include Hidden Markov Models (HMMs) [1][2][3][4], Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTMs) and their variants: Bi-directional LSTMs (BLSTMs) and Multi-Dimensional LSTMs (MDLSTMs) [5]. HMMs enable embedded training and can be robust to noise and linear distortions.…”

Section: Introductionmentioning

confidence: 99%

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

et al. 2018

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Abstract:The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.

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Section: Introductionmentioning

confidence: 99%

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

et al. 2018

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“…Nevertheless up to our knowledge no papers tackling the problem of training a handwriting 4.8 TUM [26] 9.0 UPV [26] 16.8 ParisTech [26] 23.7 IRISA [26] 25.3 SIEMENS [26] 26.8 recognition system in an unsupervised manner have been published so far in the literature. Our system is a pure HMM system, yet it has been shown that an HMM system can be improved by using neural networks [19] or system combination [18]. The systems proposed by A2iA [18], UPV and ParisTech [26] were combinations of different classifiers including recurrent neural networks and HMMs.…”

Section: Discussionmentioning

confidence: 99%

Towards Unsupervised Learning for Handwriting Recognition

Kozielski

Nuhn

Doetsch

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Abstract-We present a method for training an off-line handwriting recognition system in an unsupervised manner. For an isolated word recognition task, we are able to bootstrap the system without any annotated data. We then retrain the system using the best hypothesis from a previous recognition pass in an iterative fashion. Our approach relies only on a prior language model and does not depend on an explicit segmentation of words into characters. The resulting system shows a promising performance on a standard dataset in comparison to a system trained in a supervised fashion for the same amount of training data.

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“…Contextual and dynamic information is used in [11] for handwriting recognition. Feature extraction is performed using a dynamic approach using derivative features.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Context Dependent Modeling for Arabic Handwriting Recognition

Hamdani

Doetsch

Ney

2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Abstract-This paper proposes the improvement of context dependent modeling for Arabic handwriting recognition. Since the number of parameters in context dependent models is huge, CART trees are used for state tying. This work is based on a new set of questions for the CART tree construction based on a "lossy mapping" categorization of the Arabic shapes. The used system is a combination of Hidden Markov Models and Recurrent Neural Networks using the hybrid approach. A comparison between a Neural network trained using the baseline labels and another one based on the CART tree labels is done. The experimental results show that the use of the CART labels for the Neural Network training beneficial. The lossy mapping based CART tree performed better than the baseline system. An absolute improvement of 2.9% in terms of Word Error Rate is performed on the test set of the OpenHaRT database.

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Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition

Cited by 113 publications

References 32 publications

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

Towards Unsupervised Learning for Handwriting Recognition

Improvement of Context Dependent Modeling for Arabic Handwriting Recognition

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