Research on Uyghur Handwriting Identification Technology Based on Stroke Statistical Features

The automatic handwriting identification is a hot topic in pattern recognition that it has been extensively studied in many languages. A Uyghur handwriting identification technique based on the stroke statistical features is proposed in this paper. Firstly, the handwriting image is preprocessed taking modified methods of grid line removal, noise reduction and thinning. Then novel stroke statistical features are extracted based on the structural character and writing styles of Uyghur handwriting. And this approach respectively achieves a top 1 and top 2 identification rates of 98.66% and 99.78% on the Uyghur handwriting data set from 224 different people. Finally, Comparison analysis of different stroke length and distance measurement method has been conducted through three different kinds of experiments, the optimal stroke length and distance measurement method is determined, and its effectiveness and stability are tested. The stroke statistical features can capture the structural character and writing style of Uyghur handwriting efficiently, and it is suitable for any languages theoretically.