On-line Handwritten Isolated Symbol Recognition using Bidirectional Long Short-term Memory (BLSTM) Networks

Abstract : In this article, we studied BLSTM networks to recognize on-line handwritten isolated symbols (including both digits and mathematical symbols). BLSTM networks are suitable for sequence classification tasks as they can access the contextual information from two directions. We tested the performance of the BLSTM model for two different problems using well known datasets and compared our results to the state of the art on the same datasets.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01576309
Contributor : Harold Mouchère <>
Submitted on : Tuesday, August 22, 2017 - 6:52:56 PM
Last modification on : Wednesday, December 19, 2018 - 3:02:05 PM

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Ting Zhang, Harold Mouchère, Christian Viard-Gaudin. On-line Handwritten Isolated Symbol Recognition using Bidirectional Long Short-term Memory (BLSTM) Networks. Third Sino-French Workshop on Education and Research collaborations in Information and Communication Technologies SIFWICT 2015, Jun 2015, Nantes, France. ⟨hal-01576309⟩

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