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handwriting recognition相关的网络例句

查询词典 handwriting recognition

与 handwriting recognition 相关的网络例句 [注:此内容来源于网络,仅供参考]

This improves handwriting and reinforces word recognition and spelling.

这有助于书写,提高单词的认识和拼写。

Some experiments of handwriting capital English letter recognition is taken and better result is obtained.

本文以手写体大写英文字符的识别为例介绍该方法,通过实验证明该方法具有较好的识别效果。

This paper is focus on small set character recognition. Based on skeleton graphics, handwriting Chinese character is defined as a set of isolated branch, isolated loop and component. Relation among these three elements is also defined. Then, mathematical model is raised.

本文针对小类别数手写汉字,在骨架图形的基础上,把手写汉字看作孤枝、孤环和部件的集合,并定义三者之间的方位关系,从而建立手写汉字的数学模型。

Developed a handwriting character recognition system, on-line input signatures can identify numbers, letters and Chinese characters.

用VC++6.0开发的一个手写字符识别系统,联机输入签名,可以识别数字,字母和汉字。

As feature extraction acts an important role in pattern recognition system, we propose three novel feature extraction methods for HCCR: a、Two structural feature extraction methods, Stroke Cross Counting Features and Peripheral Features are studied. According to the characteristics of Chinese handwriting samples, we extend the SCCF and PF respectively to weighted elastic SCCF and weighted elastic PF. Experiments show that the modified feature extraction approaches are better than original approaches. b、A new statistical feature extraction method——Elastic Meshing Directional Decomposition Feature extraction is proposed. According to the stroke statistical properties of Chinese character, we first decompose a handwritten character pattern into four directional subpatterns. Then a set of elastic meshes are applied to each of the four subpatterns respectively to extract the pixel distribution features.

统计识别方法:利用统计模式识别的经典方法——Bayes分类方法来进行汉字识别的研究,特征提取是统计模式识别的关键,本文着重围绕手写体汉字的特征分析和提取方法进行了研究,提出了三种特征提取的新方法: a、两种改进的结构特征提取方法:在笔划密度特征和外围特征的基础上,结合手写体汉字的特点,提出了加权非均匀笔划密度特征、加权非均匀外围特征的特征提取新方案; b、一种新的统计特征提取方法——弹性网格方向分解特征,根据汉字方块字的特点及笔划分布的统计特征,对手写体汉字进行横竖撇捺四方向的分解,并利用一系列的弹性网格对汉字分解后各分量的象素进行统计而提取特征,实验证明该特征提取方法用来进行手写体汉字识别是十分可行和有效的。

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