Investigation on deep learning for off-line handwritten Arabic character recognition

Abstract

Handwritten character recognition is a system widely used in the modern world and it is still an important challenge. Traditional machine-learning techniques require careful engineering and considerable domain expertise to transform raw data into a feature vector from which the classifier could classify the input pattern. To cope with this problem, the popular Deep Convolutional Neural Networks (DCNN), introduced recently, have effectively replaced the hand-crafted descriptors with network features and have been shown to provide significantly better results than traditional methods. It is one of the fastest growing areas in machine learning, promising to reshape the future of artificial intelligence. However, the problem with deep learning is that it requires large datasets for training because of the huge number of parameters needed to be tuned by a learning algorithm. CNN model can be used in three different ways: (i) training the CNN from scratch; (ii) using the transfer learning strategy to leverage features from a pre-trained model on a larger dataset; and (iii) keeping the transfer learning strategy and fine-tune the weights of CNN architecture. In this work, we investigate the applicability of DCNN using transfer learning strategies on two datasets; a new expanded version of our recently proposed database for off-line isolated handwritten Arabic character, referred to as OIHACDB and AHCD. Our results showed satisfactory recognition accuracies and outperform all other prominent exiting methods in the field of Handwritten Arabic Character Recognition (HACR).