Enhanced technique for Arabic handwriting recognition using deep belief network and a morphological algorithm for solving ligature segmentation

• Main Authors: Nada Essa, Eman El‐Daydamony, Ahmed Atwan Mohamed
• Format: Article
• Language: English
• Published: Electronics and Telecommunications Research Institute (ETRI) 2018-12-01
• Series: ETRI Journal
• Subjects: deep belief networks; deep learning; ligatures; morphology; restricted Boltzmann machine
• Online Access: https://doi.org/10.4218/etrij.2017-0248

Arabic handwriting segmentation and recognition is an area of research that has not yet been fully understood. Dealing with Arabic ligature segmentation, where the Arabic characters are connected and unconstrained naturally, is one of the fundamental problems when dealing with the Arabic script. Arabic character‐recognition techniques consider ligatures as new classes in addition to the classes of the Arabic characters. This paper introduces an enhanced technique for Arabic handwriting recognition using the deep belief network (DBN) and a new morphological algorithm for ligature segmentation. There are two main stages for the implementation of this technique. The first stage involves an enhanced technique of the Sari segmentation algorithm, where a new ligature segmentation algorithm is developed. The second stage involves the Arabic character recognition using DBNs and support vector machines (SVMs). The two stages are tested on the IFN/ENIT and HACDB databases, and the results obtained proved the effectiveness of the proposed algorithm compared with other existing systems.