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A Lexicon Verification Strategy in a BLSTM Cascade Framework

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Stuner Bruno
  • Function : Author
Chatelain Clement

Abstract

Handwriting recognition always has been a difficult problem, with image related problems on the one hand and language processing on the other hand. Significant improvements have been made in handwriting recognition thanks to new recurrent neural networks based on LSTM cells. The high character recognition performances of these networks are almost systematically combined with linguistic knowledge, that is to say lexicon driven decoding method, to correct character misrecognitions. However with such high performance, we wonder on the possibility to use them without lexical decoding for word recognition. In this article, we explore this idea by proposing a lexicon verification strategy that provides a very low error rate, while conceding a consequent amount of rejects. Therefore, this verification approach perfectly fits in a cascade framework, where the rejects of a classifier are processed by the next cascade's classifier. The resulting system is nearly insensitive to the lexicon size, while providing a much faster decoding process than a standard lexicon driven decoding. Furthermore, when processing the final rejects of the cascade by a basic lexical decoding, our approach reach state of the art performance for isolated word recognition.
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Dates and versions

hal-02075759 , version 1 (21-03-2019)

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Stuner Bruno, Chatelain Clement, Paquet Thierry. A Lexicon Verification Strategy in a BLSTM Cascade Framework. 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), Oct 2016, Shenzhen, China. pp.234-239, ⟨10.1109/ICFHR.2016.0053⟩. ⟨hal-02075759⟩
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