On-line Handwritten Uyghur Word Recognition Using Segmentation-Based Techniques

Abstract

An approach to online handwriting word recognition using segmentation-based techniques is presented in this paper. This approach is referred to as lexicon-driven approach because an optimal segmentation is generated for each string in the lexicon. Word recognition problem is transformed into matching optimization problems between the dictionary entry and the handwritten word image. The segmentation processes use these steps such as removing delayed strokes, shape analysis of the stroke trajectory, reconstructing delayed strokes and combining adjacent fragments. Dynamic matching is used to ranking the lexicon entries in order to get best match. A match score is assigned to a segmentation and string by matching each segment to the corresponding character in the string with a character recognition algorithm that returns confidence value for each character class. As a result the performance for lexicons of size 10, 100, 500 and 1000are 93.17%, 70.33%, 59.79%，51.20% and 94.85%, 79.75%, 74.42%, 62.19% for adding distance and normalizing distance respectively.