Improving end-to-end deep learning methods for Arabic handwriting recognition

Abstract

The number of Arabic handwriting documents has increased greatly. Studies conducted in the Arabic handwriting recognition field have progressed significantly in recent years in different areas. The existing studies for Arabic language compared to others such as Latin remains insufficient. During the last decade, neural networks (NNs) have become the de facto standard for deep learning, and the combination of two or more NNs has proved its ability to learn complex objects, such as handwriting, and emulate human brains. We proposed an approach combining convolutional NN and bidirectional long short-term memory for recognition. This unique approach makes it possible to recognize Arabic handwriting words without segmentation. The proposed architecture is very efficient in terms of accuracy for Arabic word recognition. The hyperparameters set for our model were chosen based on a structured process of randomness and grid search to increase the accuracy of the model.