Touching Character Segmentation Method of Archaic Lanna Script

Abstract

In general, character recognition consists of four stages: image preprocessing, segmentation, feature extraction, and classification. Character segmentation is one of the most important and difficult tasks in character recognition. Incorrectly segmented characters are not likely to be correctly recognized. Touching characters, which always arises when handwritten characters are being segmented, makes the task even more difficult. Therefore, this paper emphasizes the interest to the segmentation of touching and overlapping characters. This paper proposes two new techniques which are shown to dramatically improve the segmentation accuracy. The first proposed technique emphasizes on converting a greyscale image to a binary image while the second proposed technique emphasizes on the process of character segmentation itself. In the proposed character segmentation process, the bounding box analysis is initially employed to segment the document image into images of isolated characters and images of touching characters. The thinning algorithm is applied to extract the skeleton of the touching characters. Next, the skeleton of the touching characters is separated into several pieces. Finally, the separated pieces of the touching characters are put back to reconstruct two isolated characters. The proposed algorithm achieves an accuracy of 89.26%.