Character Recognition of Arabic Handwritten Characters Using Deep Learning
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Abstract
Optical character recognition (OCR) is used to digitize texts in printed documents and camera images. The most basic step in the OCR process is character recognition. The Arabic language is more complex than other alphabets, as the cursive is written in cursive and the characters have different spellings. Our research has improved a character recognition model for Arabic texts with 28 different characters. Character recognition was performed using Convolutional Neural Network models, which are accepted as effective in image processing and recognition. Three different CNN models have been proposed. In the study, training and testing of the models were carried out using the Hijja data set. Among the proposed models, Model C with a 99.3% accuracy rate has obtained results that can compete with the studies in the literature.
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Journal of Studies in Science and Engineering is licensed under a Creative Commons Attribution License 4.0 (CC BY-4.0).
References
Zhong, X., ShafieiBavani, E. and Jimeno Yepes, A., 2020, August. Image-based table recognition: data, model, and evaluation. In European Conference on Computer Vision (pp. 564-580). Springer, Cham.
Altwaijry, N. and Al-Turaiki, I., 2021. Arabic handwriting recognition system using convolutional neural net-work. Neural Computing and Applications, 33(7), pp.2249-2261.
Sakal, M., 2020. Örgütlenmenin yeni formları olarak sanal örgütlerde bilgi yönetimi: Çölyak hastalarının örgü-tlenmesi için model önerisi.
Yavuz, A., 2021. Derin öğrenme algoritmaları ile trafik işaret ve levhalarının tanımlanması (Master's thesis, Pamukkale Üniversitesi Fen Bilimleri Enstitüsü).
Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N. and Amiri, H., 2002, October. IFN/ENIT-database of handwritten Arabic words. In Proc. of CIFED (Vol. 2, pp. 127-136). Citeseer.
Ramdan, J., Omar, K., Faidzul, M. and Mady, A., 2013. Arabic handwriting database for text recognition. Procedia Technology, 11, pp.580-584.
Ahmed, R., Dashtipour, K., Gogate, M., Raza, A., Zhang, R., Huang, K., Hawalah, A., Adeel, A., and Hussain, A., 2019, July. Offline Arabic handwriting recognition using deep machine learning: A review of recent advances. In International Conference on Brain Inspired Cognitive Systems (pp. 457-468). Springer, Cham.
Bhuiyan, R.A., Tushar, A.K., Ashiquzzaman, A., Shin, J. and Islam, M.R., 2017, December. Reduction of gesture feature dimension for improving the hand gesture recognition performance of numerical sign language. In 2017 20th International Conference of Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.
Sarangi, L., Mohanty, M.N., and Pattanayak, S., 2016. Design of MLP based model for analysis of patient suffering from influenza. Procedia Computer Science, 92, pp.396-403.
Latif, G., Alghazo, J., Alzubaidi, L., Naseer, M.M. and Alghazo, Y., 2018, March. Deep convolutional neural network for recognition of unified multi-language handwritten numerals. In 2018 IEEE 2nd International work-shop on Arabic and derived script analysis and recognition (ASAR) (pp. 90-95). IEEE.
Mars, A. and Antoniadis, G., 2016. Arabic online handwriting recognition using neural network. International Journal of Artificial Intelligence and Applications (IJAIA), 7(5).
Elleuch, M., Maalej, R. and Kherallah, M., 2016. A new design based-SVM on the CNN classifier architecture with dropout for offline Arabic handwritten recognition. Procedia Computer Science, 80, pp.1712-1723.
El-Sawy, A., El-Bakry, H. and Loey, M., 2016, October. CNN for handwritten Arabic digits recognition based on LeNet-5. In International conference on advanced intelligent systems and informatics (pp. 566-575). Springer, Cham.
Hassen, H., Al-Madeed, S., and Bouridane, A., 2021. Subword Recognition in Historical Arabic Documents using C-GRUs.
[ Mudhsh, M. and Almodfer, R., 2017. Arabic handwritten alphanumeric character recognition using a very deep neural network. Information, 8(3), p.105.
Younis, K.S., 2017. Arabic handwritten character recognition based on deep convolutional neural net-works. Jordanian Journal of Computers and Information Technology (JJCIT), 3(3), pp.186-200.
Altwaijry, N. and Al-Turaiki, I., 2021. Arabic handwriting recognition system using convolutional neural net-work. Neural Computing and Applications, 33(7), pp.2249-2261.