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中文摘要：
无切分维吾尔文文档识别技术能够有效避免字符切分错误,但是对于低数据资源的新样本类型,原有模型往往难以获得较高的识别性能。为此,该文提出共享常用维文字体间相对稳定的字符结构信息,并用Bootstrap方法提高样本利用效率的解决方法。通过在实际书籍样本上的实验表明,仅利用规模约原始训练样本1/5的新类型样本,该方法在测试集上的平均字符识别准确率就可以达到95.05%;而与常用的最大后验概率估计方法相比,也能使识别错误率相对降低55.76%-63.84%。因此,该方法能够有效解决低数据资源条件下的维文字符建模问题,实现对新样本类型的高性能识别。
英文摘要：
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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