Affiliations: [a] Higher Institute of Industrial Management (ISGIS), University of Sfax, Sfax, Tunisia | [b] Medical School of Sfax, University of Sfax, Sfax, Tunisia | [c] National School of Computer Science (ENSI), University of Manouba, Tunis, Tunisia | [d] Faculty of Sciences, University of Sfax, Sfax, Tunisia
Correspondence:
[*]
Corresponding author: Anis Mezghani, Higher Institute of Industrial Management (ISGIS), University of Sfax, Sfax, Tunisia. E-mail: anis.mezghani@gmail.com.
Abstract: Handwritten text recognition remains a popular area of research. An analysis of these techniques is more necessary. This article is practically interested in a bibliographic study on existing recognition systems with the aim of motivating researchers to look into these techniques and try to develop more advanced ones. It presents a detailed comparative study carried out on some Arabic handwritten character recognition techniques using holistic, analytical and a segmentation-free approaches. In this study, first, we show the difference between different recognition approaches: deep learning vs machine learning. Secondly, a description of the Arabic handwriting recognition process regrouping pre-processing, feature extraction and segmentation was presented. Then, we illustrate the main techniques used in the field of handwriting recognition and we make a synthesis of these methods.
