A Dynamic Programming Method for Segmentation of Online Cursive Uyghur Handwritten Words into Basic Recognizable Units

Correct and efficient segmentation of Uyghur words into characters is crucial to the successful recognition. However, little work has been done in this area. There are many connected characters in cursive Uyghur handwriting, which makes the segmentation and recognition of Uyghur words very difficult. To enable large vocabulary Uyghur word recognition using character models, we propose a character segmentation method using dynamic programming in online cursive Uyghur handwriting. Firstly, after removing delayed strokes from the handwritten words, potential breakpoints are detected from concavities and ligatures by temporal and shape analysis of the stroke trajectory. Then, a dynamic programming method is applied to find the best segmentation point for each character. Our preliminary experiments on an online Uyghur word dataset demonstrate that the proposed method can achieve good performance in segmenting cursive handwritten Uyghur characters.