Arabic Handwritten Character Recognition Using Convolutional Neural Networks

Abstract

The Arabic language is one of the six most important languages in the world. Because more than 420 million people worldwide use the Arabic script, research into the recognition of Arabic handwriting is crucial. The demand for software that can automatically read and interpret Arabic Handwriting has been rapidly expanding in recent years as the use of digital devices has become increasingly widespread. Characters are written by Hands in Arabic are more difficult to decipher than those noted in English or other languages because of the nature of the words used in Arabic. In this study, we designed a new model, Convolutional Neural Network 14 Layers (CNN-14), to recog-nise handwritten Arabic characters. The The model was trained and tested on the Arabic Handwritten Character Dataset (AHCD) and Hijja datasets, The proposed model achieved good results, with an accuracy of 99.36 per cent in AHCD and 94.35 per cent Hijja dataset.

A Deep Learning Approach to Convert Handwritten Arabic Text to Digital Form

“…Recognition , especially when dealing with datasets containing characters and diverse writing styles, this is shown in a study [7] and [16] and [17].…”