Character model optimization for segmentation-free Uyghur text line recognition
JIANG Zhiwei, DING Xiaoqing, PENG Liangrui
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Abstract：A text line recognition method was developed without pre-segmentation using a hidden Markov model (HMM) for simultaneously segmenting and recognizing text line images. The algorithm uses a probability graph to reduce recognition error from failed pre-segmentation results. However, the HMM design and training is complicated and the HMM generalization performance can not be easily improved in multi-font texts. Therefore, a character model optimization method with reasonably clustered observations was developed based on the most common HMM state in images. Then, a method was developed to optimize the model structure and parameters together for a multi-font Uyghur text line recognition system. Tests show that this method improves the state allocation, the generalization performance and the state efficiency of the character model for multi-font texts.
姜志威, 丁晓青, 彭良瑞. 针对无切分维吾尔文文本行识别的字符模型优化[J]. 清华大学学报（自然科学版）, 2015, 55(8): 873-877,883.
JIANG Zhiwei, DING Xiaoqing, PENG Liangrui. Character model optimization for segmentation-free Uyghur text line recognition. Journal of Tsinghua University(Science and Technology), 2015, 55(8): 873-877,883.
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