Deep Learning Applications to Offline Arabic Handwriting Words Recognition Using Convolutional Neural Network

Abstract

Automatic handwriting recognition is the process of converting online and offline letters or words as a graphical form into its text format. Automatic Arabic Handwriting words recognition using deep learning neural networks is still in the early stages in terms of research. There are no general, complete, and reliable Arabic Handwritten Words (AHW) database (lexicon) that can be used as a reference or a benchmark for all researchers who want to extend the work on automatic Arabic handwriting word recognition. Also, many historic Arabic manuscripts have deteriorated because of inappropriate storage and most of them have not been digitized due to the lack of reliable database that can be used to recognize the words of Arabic manuscripts. Deep Convolutional Neural Networks (DCNNs) can be used to solve the problems of automatic Arabic handwriting words recognition. In this work, a new DCNN algorithm applied to a new dataset of Handwritten Arabic words representing the seven days of the week named Arabic Handwritten Weekdays Dataset (AHWD) has been programmed, tested, and analyzed