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- W3102014087 abstract "AbstractIn 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.KeywordsStrokeVelocityMPDRewardPolicyEpisodes" @default.
- W3102014087 created "2020-11-23" @default.
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- W3102014087 date "2020-01-01" @default.
- W3102014087 modified "2023-09-27" @default.
- W3102014087 title "Towards Online Handwriting Recognition System Based on Reinforcement Learning Theory" @default.
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- W3102014087 doi "https://doi.org/10.1007/978-3-030-63820-7_64" @default.
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