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
(Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China)
(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)
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.
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