Character model optimization for segmentation-free Uyghur text line recognition

JIANG Zhiwei; DING Xiaoqing; PENG Liangrui

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Journal of Tsinghua University(Science and Technology) ›› 2015, Vol. 55 ›› Issue (8) : 873-877,883.

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Character model optimization for segmentation-free Uyghur text line recognition

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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.

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information processing / character recognition / hidden Markov model (HMM) / statistical learning / Uyghur

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JIANG Zhiwei, DING Xiaoqing, PENG Liangrui. Character model optimization for segmentation-free Uyghur text line recognition[J]. Journal of Tsinghua University(Science and Technology). 2015, 55(8): 873-877,883

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