Arabic Hand-Written Character Recognition Based on Deep Convolutional Neural Networks
khaled younis, abdullah khateeb.
Abstract
The automatic analysis and recognition of off-line Arabic handwritten characters from images is an important problem in many applications. Even with the great progress of recent research in optical character recognition, a few problems still wait to be solved, especially for Arabic characters. The emergence of Deep Neural Networks promises a strong solution to some of these problems. We present a deep neural network for the handwritten Arabic character recognition problem that uses convolutional neural network (CNN) models with regularization parameters such as batch normalization to prevent overfitting. We applied the Deep CNN for the AIA9k and the AHCD databases and the classification accuracy for the two datasets were 94.8% and 97.6%, respectively. A study of the network performance on the EMNIST and a form-based AHCD dataset were performed to aid in the analysis.
Key words: Convolutional Neural Network, Deep Learning, Optical Character Recognition, Arabic Handwritten Character Recognition
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