Mixed Thai-English Character Classification Based on Histogram of Oriented Gradient Feature

The task of classifying mixed Thai-English characters carries considerable challenges due to the number and complexity of the characters. This paper proposes and empirically investigates the performance of a classification system that uses Histogram of Oriented Gradient as an image feature with Support Vector Machine as a classification tool. The experiments were done on the datasets provided by NECTEC which consists of over 600,000 printed images of individual characters from 142 distinct classes. With this proposed method, an accuracy of 97% can be achieved without a look up dictionary or any post-processing system.