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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 |
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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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| Keywords
information processing
character recognition
hidden Markov model (HMM)
statistical learning
Uyghur
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Issue Date: 15 August 2015
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2015,
Vol. 55
