Abstract: For 128 Uyghur characters, a handwritten recognition algorithm based on radical dictionary and time division direction feature is proposed. First, the radical dictionary is established by decomposing the Uyghur characters as three type radicals, main, affix and dot. The problem of stroke order and connection can be solved by analysis of the connected and broken strokes. Second, a new online statistical feature, named time division directional feature, is extracted from every radical to reduce the interference from handwritten topological deformation. The statistical feature is designed by dividing freeman codes according to time sequence, with its short term variations being found and adjusted to correct the dither phenomenon. Finally, different classifications are designed for various types of radicals. With the radical coefficient estimated according to the coefficient distribution, the character recognition result is obtained by fusing the outputs of all classifications using the weighted naive Bayesian algorithm. Experiment results show that the algorithm can effectively identify 128 unconstrained handwritten Uyghur characters. An average recognition rate of 93.15% is achieved on the Uyghur character database containing 13056 samples.

Key words: computer application, handwriting recognition, uyghur language, radical dictionary, directional feature, naive Bayes