Gabor-Based Recognizer for Chinese Handwriting from Segmentation-Free Strategy
Gabor-Based Recognizer for Chinese Handwriting from Segmentation-Free Strategy
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基于Gabor的无分割策略中文手写识别器
DOI：
10.1007/978-3-540-74272-2_67
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Hu
中科院分区：
文献类型：
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作者：
Tonghua Su;Tianwen Zhang;D. Guan;Hu
提出了一种基于Gabor特征和隐马尔可夫模型的中文手写体文本识别器。首先通过滑动窗口提取文本行并过滤为Gabor观测值。然后采用Baum-Welch算法训练字符Humble。最后通过Viterbi算法找出最大后验概率准则下的最佳字符串作为输出。实验是在一组汉字笔迹上进行的。实验结果不仅证明了无分割策略的可行性，而且也体现了Gabor滤波器在中文手写体转写中的优势。
Segmentation-free recognizer is presented to transcribe Chinese handwritten documents, incorporating Gabor features and Hidden Markov Models (HMMs). Textline is extracted and filtered as Gabor observations by sliding windows first. Then Baum-Welch algorithm is used to train character HMMs. Finally, best character string in maximizing a posteriori criterion is found out through Viterbi algorithm as output. Experiments are conducted on a collection of Chinese handwriting. The results not only show the evident feasibility of segmentation-free strategy, but also manifest the advantages of Gabor filters in the transcription of Chinese handwriting.