低数据资源条件下基于结构信息共享的无切分维文文档识别字符建模

doi:10.11999/JEIT150019

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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.

Key words： Character recognition Hidden Markov Model (HMM) Statistical learning Uyghur character

Received: 06 January 2015 Published: 29 June 2015

PACS:

TP391.43

Corresponding Authors: Jiang Zhi-wei E-mail: jiangzw@ocrserv.ee.tsinghua.edu.cn

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

Cite this article:

Jiang Zhi-wei,Ding Xiao-qing,Peng Liang-rui等. Uyghur Character Models with Shared Structure Information for Segmentation-free Recognition under Low Data Resource Conditions[J]. JEIT, 2015, 37(9): 2103-2109.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT150019 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I9/2103

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