首页> 外文会议>International Conference on Computer Analysis of Images and Patterns(CAIP 2007); 20070827-29; Vienna(AT) >Gabor-Based Recognizer for Chinese Handwriting from Segmentation-Free Strategy
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Gabor-Based Recognizer for Chinese Handwriting from Segmentation-Free Strategy

机译:基于Gabor的无分段策略的​​中文手写识别器

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摘要

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.
机译:提出了无分段识别器,用于转录中文手写文档,并结合了Gabor功能和隐马尔可夫模型(HMM)。首先通过滑动窗口来提取文本行并将其过滤为Gabor观测值。然后使用Baum-Welch算法训练字符HMM。最后,通过维特比算法找到了最大化后验准则的最佳字符串作为输出。实验是对一系列中文笔迹进行的。结果不仅显示了无分割策略的明显可行性,而且还显示了Gabor过滤器在中文笔迹转录中的优势。

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