Arabic Handwritten Recognition System Using Deep Convolutional Neural Networks

Abstract

The use of traditional chalkboards, the illnesses that result from the use of chalk such as allergies, students who find it difficult to copy their lessons written by the teacher, make our daily classes lack intelligence, however, these difficulties manifest themselves in the difficult recognition of handwritten letters and words. Hence, we are discussing these current problems by providing future solutions that are smart, to use a new term it is "the smart classroom". This has prompted extensive research into the use of deep learning in the fields of computer vision and image processing based on artificial neural networks. In this article, a new approach to the recognition of Arabic handwritten words is presented. Certainly, the recognition of Arabic handwriting is a very recent research topic in computer vision, which has several attractive applications, in particular intelligent systems, video conferencing, real-time applications etc.… In our proposed approach, Deep Convolutional Neural Networks (DCNN) are adapted to perform the classification phase. Notably the architecture "ResNet", and "VGG16", using an enriched dataset containing images extracted from the IFN/ENIT database (The names of Tunisian villages). This technique has shown very good performance for the frequent recognition problems. According to the results obtained, the developed system gives very interesting recognition rates.