Abstract
Arabic handwriting recognition is considered among the most important and challenging recognition research subjects due to the cursive nature of writing and the similarities between different characters shapes. In this paper, we investigate the problem of handwritten Arabic recognition. We propose a new architecture combining CNN and BLSTM based on character model approach with CTC decoder. The handwriting Arabic database KHATT is used for experiments. The results demonstrate a net advantage of performance for the CNN-BLSTM combining approach compared to the approaches used in the literature.
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Noubigh, Z., Mezghani, A., Kherallah, M. (2021). Contribution on Arabic Handwriting Recognition Using Deep Neural Network. In: Abraham, A., Shandilya, S., Garcia-Hernandez, L., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2019. Advances in Intelligent Systems and Computing, vol 1179. Springer, Cham. https://doi.org/10.1007/978-3-030-49336-3_13
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DOI: https://doi.org/10.1007/978-3-030-49336-3_13
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