A Comparative Study of Persian/Arabic Handwritten Character Recognition

In recent years, many techniques for the recognition of Persian/Arabic handwritten documents have been proposed by researchers. To test the promises of different features extraction and classification methods and to provide a new benchmark for future research, in this paper a comparative study of Persian/Arabic handwritten character recognition using different feature sets and classifiers is presented. Feature sets used in this study are computed based on gradient, directional chain code, shadow, under-sampled bitmap, intersection/junction/endpoint, and line-fitting information. Support Vector Machines (SVMs), Nearest Neighbour (NN), k-Nearest Neighbour (k-NN) are used as different classifiers. We evaluated the proposed systems on a standard dataset of Persian handwritten characters. Using 36682 samples for training, we tested the proposed recognition systems on other 15338 samples and their detailed results are reported. The best correct recognition of 96.91% is obtained in this comparative study.

I. Introduction

Many potential applications such as postal automation, Bank cheque processing, and automatic data entry depend on handwritten character recognition. These applications promise the recognition of handwritten characters to be an active research area for many years and at the outset high accuracies of handwritten character recognition have been reported for some languages such as English, Chinese, and Japanese languages [1], [2].