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2020 | OriginalPaper | Chapter

Towards Online Handwriting Recognition System Based on Reinforcement Learning Theory

Authors : Ramzi Zouari, Houcine Boubaker, Monji Kherallah

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

In this work, we formalize the problem of online handwriting recognition according to the reinforcement learning theory. The handwriting trajectory is divided into strokes and we extracted their structural and parametric features based on freeman codes, visual codes and beta-elliptic features respectively. The environments were trained using tabular q-learning algorithm in order to calculate the optimal sate-to-action values for each class of handwriting. The proposed model was evaluated on LMCA database and achieved very promising results for both structural and parametric representations.

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previous chapter SpotFast Networks with Memory Augmented Lateral Transformers for Lipreading

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Title: Towards Online Handwriting Recognition System Based on Reinforcement Learning Theory
Authors: Ramzi Zouari
Houcine Boubaker
Monji Kherallah
Publisher: Springer International Publishing
Book: Neural Information Processing
Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63820-7_64

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