Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions

Jiang Zhi-wei; Ding Xiao-qing; Peng Liang-rui; Liu Chang-song

doi:10.11999/JEIT150019

Volume 37 Issue 9

Sep. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(9): 2103-2109

Jiang Zhi-wei, Ding Xiao-qing, Peng Liang-rui, Liu Chang-song. Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2103-2109. doi: 10.11999/JEIT150019

Citation:

Jiang Zhi-wei, Ding Xiao-qing, Peng Liang-rui, Liu Chang-song. Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2103-2109. doi: 10.11999/JEIT150019

Citation:

Jiang Zhi-wei, Ding Xiao-qing, Peng Liang-rui, Liu Chang-song. Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2103-2109. doi: 10.11999/JEIT150019

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Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions

doi: 10.11999/JEIT150019

Received Date: 2015-01-06
Rev Recd Date: 2016-03-25
Publish Date: 2015-09-19

Abstract

Abstract

Although segmentation-free Uyghur character document recognition can efficiently avoid character segmentation error, it does not work well on low-resource new-type samples. This paper suggests sharing stable character structure among different Uyghur fonts, and improves the efficiency of utilizing samples through Bootstrap. Experiments are made on new-type book samples, which contains only 1/5 training sample amount than the original. The average character recognition accuracy of the proposed method on test samples is 95.05%, and has 55.76%~63.84% recognition error rate relative decrease than the one of Maximum A Posteriori (MAP) method. Therefore, the proposed method can accomplish accurate Uyghur character model training under low data resource conditions.
- Character recognition,
- Hidden Markov Model (HMM),
- Statistical learning,
- Uyghur character

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References(20)

References

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