CAJ | 학술논문

以基于隐马尔可夫模型和统计语言模型的研究作为基础，着重研究联机手写哈萨克文的切分技术、连体段分类和特征参数的独特提取技术。系统先将提取延迟笔划后的连体段主笔划作为HMM识别器的输入，再根据被识别的主笔划的编号和延迟笔划标记从连体段分类词典中查找，找到对应的连体段识别结果。通过去除连体段延迟笔画的方法可以有效地减少需建立的模型数目，进而提高识别速度和避免由字符切分所带来的问题。
이기우은마이가부모형화통계어언모형적연구작위기출，착중연구련궤수사합살극문적절분기술、련체단분류화특정삼수적독특제취기술。계통선장제취연지필화후적련체단주필화작위HMM식별기적수입，재근거피식별적주필화적편호화연지필화표기종련체단분류사전중사조，조도대응적련체단식별결과。통과거제련체단연지필화적방법가이유효지감소수건립적모형수목，진이제고식별속도화피면유자부절분소대래적문제。
Based on Hidden Markov Model(HMM)and Statistical Language Model(SLM), this paper focuses on the techniques of segmentation, word-part classification and feature extraction for Kazakh online handwriting recognition. The delayed strokes are removed from sub-words and then it takes the main strokes as the input of HMM recognizer. After that, the paper searches the result in sub-word classification dictionary according to the number of identified main strokes and delayed strokes. By removing the delayed-strokes, it can not only improve the recognition speed of system, but also can reduce the number of models and avoid some problems caused by segmentation.