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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Zouari, R., Boubaker, H., Kherallah, M. (2020). Towards Online Handwriting Recognition System Based on Reinforcement Learning Theory. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_64
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