STRUCTURAL DESCRIPTION TO RECOGNIZING HAND-PRINTED ARABIC CHARACTERS USING DECISION TREE LEARNING TECHNIQUES

A. Amin and N. Al-Darwish

Keywords

Pattern recognition, Arabic characters, handprinted characters, parallel thinning, feature extraction, structural classification, MachineLearning

Abstract

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification, and a large variety of banking, business, and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries that are tedious to construct and diffcult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example. The system was tested on a sample of handwritten characters from several individuals whose writing quality ranged from acceptable to poor. The average recognition rate obtained using cross-validation was 87.23%.

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