Skeletonization of low -quality Chinese character ( LCC ) is a difficult problem . Since a variety of low -quality factors make traditional model cannot work properly .A novel model for LCC that is named point cloud model ( PCM ) was proposed in this paper .PCM can make full use of the existing underlying information of LCC , and the skeletonization of LCC was solved by a two-steps optimal problem.The primary skeleton segments (PSSs) of LCC were extracted based upon incremental generalized k-means clustering algorithm .The PSSs were combined within the framework of high -level Markov Model ( HMM ) .Experi-ments demonstratec the proposed method can generate “good” skeletons even in scenarios degraded with various disturbances .%低质汉字的骨架提取是骨架提取中的一个困难问题。在多种降质因素的影响下，传统骨架提取方法很难提取出“好”的骨架，本文提出利用点云模型提取低质汉字的骨架。点云模型不仅能够充分利用现有汉字的底层信息，也能够将低质汉字骨架提取转化成一个两步的优化问题。采用增量广义均值聚类方法提取出低质汉字的初始骨架；然后基于高层马可夫随机模型连接初始骨架。实验结果表明，本方法在多种降质因素影响的情况下也能够获得“好”的汉字骨架。
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