Deep Learning Algorithms for Arabic Optical Character Recognition: A Survey

Abstract

In recent years, deep learning has begun to supplant traditional machine learning algorithms in a variety of fields, including machine translation (MT), pattern recognition (PR), natural language processing (NLP), speech recognition (SR), and computer vision. Systems for optical character recognition (OCR) have recently been developed using deep learning techniques with great success. Within the area of pattern recognition and computer vision, the procedure of handwritten character recognition is still considered to be one of the most challenging. The height, orientation, and width of the handwritten characters do not always correspond with one another because different people use different writing instruments and have their own unique writing styles. This makes the job of handwritten recognition challenging and difficult. The regional languages of Arabic and Urdu have received less research. In this article, a summary and comparison of the most significant techniques of deep learning that are used in the recognition of Arabic-adapted scripts like Arabic and Urdu have been provided.