然后利用四角结构特征对手写汉字进行粗分类。
Then, Chinese character rough classification is achieved by four corner structure features.
对于结构类似的汉字，该算法可以通过特征关系予以识别，从而提高汉字的识别率。
The characters with the similar structure information can be recognized by the characteristic relation so that the recognition performance is improved.
该算法同时运用了基于统计特征和基于结构特征的汉字识别算法，并对以上两种算法做了适当的改进。
The algorithm is based on the multi-characteristics, it unifies and improves the algorithm based on the statistic characteristic and the algorithm based on the structure characteristics.
在特征提取方面，给出了汉字结构点，连通体，封闭区域，笔划等特征的提取方法。
Proposed feature extraction methods for structure, connected body, closed area and stroke in Chinese character's feature extraction.
该方法避免了一些传统汉字特征提取方法需要对图象进行二值化操作而造成的汉字字符结构信息丢失。
As a result, the proposed method avoids the binary operation used in some traditional Chinese character feature extractions that will seriously destroy the Chinese character structure.
采用过程神经元网络提取手写体汉字各类型笔形，统计各类型笔形和相交点的数量，从而建立手写体汉字特征知识的数据结构表。
The style, number and number of joint and crossover of complex strokes were accumulated, and a kind of characteristic knowledge data-base table of handwritten Chinese characters was constructed.
由于强调了汉字整体信息和相对关系的结构特征，比较成功地把握了区分手书汉字的关键因素，汉字识别率达80％以上，效果比较理想。
Becausce we emphasized the entire information, relative relationship between components in our method, we have obtained & good effect and the rate of correct recognition for RSCC comed to 80%.
为此，在汉字结构匹配时，提出了一种结构特征搜索及排序算法，很好地解决了要求无笔序输入所带来的问题。
We present a new algorithm for seeking structure feature when matching Chinese characters structure and solve the problem.
为此，在汉字结构匹配时，提出了一种结构特征搜索及排序算法，很好地解决了要求无笔序输入所带来的问题。
We present a new algorithm for seeking structure feature when matching Chinese characters structure and solve the problem.
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