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Article Dans Une Revue Multimedia Tools and Applications Année : 2020

Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon

Résumé

In this article, a handwriting recognition model whose complexity does not depend on the lexicon size is proposed. It is an alternative to lexicon-driven decoding, based on a lexicon verification process that allows to deal with millions of words, without any time consuming decoding stage. This lexicon verification is included in a cascade framework that uses complementary LSTM RNN classifiers. An original and very efficient method to obtain hundreds of complementary LSTM RNN extracted from a single training, called cohort, is proposed. The proposed approach achieves new state-of-the art performance on the Rimes and IAM datasets, and provides 90% of accuracy on the Rimes dataset when dealing with a gigantic lexicon record of 3 millions of words. The last contribution extends the idea of cohort and lexicon verification in a ROVER combination for handwriting line recognition, and achieves state-of-the-art results on the Rimes dataset.

Dates et versions

hal-02950496 , version 1 (28-09-2020)

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Citer

Bruno Stuner, Clément Chatelain, Thierry Paquet. Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon. Multimedia Tools and Applications, 2020, 79, pp.34407-34427. ⟨10.1007/s11042-020-09198-6⟩. ⟨hal-02950496⟩
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