首先对手写体字符图像进行平滑，同时获取字符的轮廓点。
The images of handwritten characters are smoothed and its contour points are obtained.
而对于手写体字符，系统采用BP神经网络来实现字符的识别。
And for the handwriting character the system adopts BP neural network to realize the recognition.
经实验表明，印刷体字符的识别准确率为95.5%，手写体字符的识别准确率为90.3%。
The experiments show that the recognition accuracy of printing character is 95.5% and the recognition accuracy of handwriting character is 90.3%.
在图象处理算法中，应用图象全局二值化方法、字符“有效行”特征的提取和双BP神经网络，对手写体字符的识别取得了良好的效果。
With the way of binarization by globe threshold, the extracting mathod of "effective-rows" feature of handwriting numerals and recognition mathod of two parallel BP networks, good result is acquired.
akaDora是一种相对简单的手写体，有这宽泛的字符支持。
AkaDora is a fairly simple script font with wide character support.
应用字符的特征矩阵设计了一个手写体汉字的分类识别算法，取得了较好的效果。
A handwritten Chinese character classifying algorithm is designed based on the character feature matrix with the excellent classifying effect.
脱机手写体汉字存在数量大、结构复杂以及变形多等问题，对其自动识别被公认为字符识别领域中难题之一。
Off-line handwritten Chinese character recognition is one of the most difficult problems in character recognition, for its huge quantity, complex structure and various transmutations.
该文使用了网格搜索和双线性搜索两种方法进行参数选择，并将两者的优点综合。应用于脱机手写体英文字符识别。
This paper uses grid search and two-line search to select the two parameters, and combines the advantage of the two methods to the application on Handwritten English Character Recognition.
脱机手写体识别是字符识别中的难点之一，日文中的平假名类似于中文的手写体草书。
Hiragana is similar to Chinese cursive writing which is a difficult topic in character recognition field.
脱机手写体识别是字符识别中的难点之一，日文中的平假名类似于中文的手写体草书。
Hiragana is similar to Chinese cursive writing which is a difficult topic in character recognition field.
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