Abstract
Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic. Due to its enormous application many works have been done in this field focusing on different languages. Arabic, being a diversified language has a huge scope of research with potential challenges. A convolutional neural network (CNN) model for recognizing handwritten numerals in Arabic language is proposed in this paper, where the dataset is subject to various augmentations in order to add robustness needed for deep learning approach. The proposed method is empowered by the presence of dropout regularization to do away with the problem of data overfitting. Moreover, suitable change is introduced in activation function to overcome the problem of vanishing gradient. With these modifications, the proposed system achieves an accuracy of 99.4% which performs better than every previous work on the dataset.
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Ashiquzzaman, A., Tushar, A.K., Rahman, A., Mohsin, F. (2019). An Efficient Recognition Method for Handwritten Arabic Numerals Using CNN with Data Augmentation and Dropout. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-13-1402-5_23
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DOI: https://doi.org/10.1007/978-981-13-1402-5_23
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