Improving Thai Optical Character Recognition Using Circular-Scan Histogram

While most previous works focus on invariant features that capture salient for higher recognition accuracy, they face an issue of misclassification among similar characters. This paper proposes a feature called circular-scan histogram that enables us to capture small salient parts of the Thai characters. With scanning distances, the distance from the edge to the first pixel on the character's boundary, a circular-scan histogram is constructed by rotating the characters during scanning, and counting frequency of each distance bin. By experiments, using approximately 60,000 single-character images of forty four Thai consonant characters with balance distribution, our proposed method can classify similar characters with accuracy of 95.72%. As baselines, we compare our method with Shape Context and Histogram of Oriented Gradient.