Abstract
Arabic is among the most spoken languages in the world today. Despite this, the optical recognition of Arabic manuscript characters by the algorithms of deep learning remains insufficient. Recently, some studies are moving towards this side and give remarkable results either for the recognition of alphabets or Arabic numbers. We highlight in this work a deep morphological gradient for the problem of recognition of Arabic manuscript digits. We use the multilayer perceptron (MLP) network used in the conference paper (Ashiquzzaman, A., & Tushar, A. K. (2017). Handwritten Arabic Numeral Recognition using Deep Learning Neural Networks), which is preceded by the morphological gradient algorithm to detect the contours of the digits. This model is applied to the database of Arabic manuscript digits available on Kaggle (Arabic Handwritten Digits Dataset), which consists of 70,000 images. The classification accuracy of the model was 99.9% with a very minimum loss of 0.09%.
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Atillah, M.E., Fazazy, K.E. (2022). Deep Morphological Gradient for Recognition of Handwritten Arabic Digits. In: Elhoseny, M., Yuan, X., Krit, Sd. (eds) Distributed Sensing and Intelligent Systems. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-64258-7_14
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DOI: https://doi.org/10.1007/978-3-030-64258-7_14
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