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特定人手写体汉字识别的方法与系统
李明敬
1995-06-01
学位类型工学博士
中文摘要手写体汉字以别研究已经有近三十年的历史,但是和实际应用仍然有一段相当在的的距离。手写体汉字识别一度被认为是一件很难的事,曾被看作是文字 识别研究的最终目标之一。它有四个主要难点:(1)汉字数量大;(2)汉字结构复杂;(3)有不少相似字;(4)不同人所写字的形状变化太大。其中,字形变化是最难解决的问题。但是,同一个人所写字的形状是相对稳定的。如果一个汉字识别系统能先适应一个人的书写风格,再识别这个人写的字,它的性能就会有很大的提高。这就是所谓的特定人手写体汉字识别,是手写体汉字识别技术实用化的一种重要手段。 本文的研究工作主要是围绕这个主题展开的。在研究分析现有手写体汉字识别方法的基础上,提出了几种简单有效的算法,并实现了一个特定人手写体汉字识别系统。 首先,提出了一种适用于特定人手写体汉字识别的线性大小归一化算法。它能保持汉字图象的长宽比及质心的相对位置不变,充分突出了特定人手写汉 字的个性。和传统的线性和非线性归一化算法相比,它可以提高识别率,而且计算非常简单。 其次,根据Burr的动态模型,提出了一种手写体汉字识别的弹性匹配法。该方法首先改变模板汉字的形状,使它尽可能地和一个未知汉字的形状重叠在一起,然后再计算它们之间的相似度。该方法还能对一个汉字的多个样本作“平均”,产生一幅中间图象作为这个字的标准模板。 考虑到汉字是一种复杂的二维形状,提出了一种基于广义Hough变换的识别方法,它从变换域抽取特征计算两个汉字的相似度,并最终以查表的方式实 现。查表法可以推广到其他模式识别问题中,以简化识别过程。 另外,还提出了场变换的概念及基于场变换的识别方法。类似于电磁场,在文字的图形中存在一种特殊的的场。这是一种生理和心理现象,和人的文字识别过程有密切的关系。这种场用一种二维标量场来描述,一个汉字的场分布是由这个汉字图象中的所有笔画点决定的。基于场变换的识别方法曾用来识别特定人的手写汉字,初步实验结果还是比较令人满意的。 最后,通过集成弹性匹配法和特征匹配法这两种小相关的方法、实现了一个高性能的特定人手写体汉字识别系统。该系统对一级圉标3,775个汉字的识 别率达到了92.05%,是近年来国内最好的。 本文的工作富于开创性。通过这些研究和开发工作,作者相信,手写体汉字识别并不象人们想象的那样难,它存在一个简单解。
英文摘要The machine recognition of handwritten Chinese characters has been studied for nearly three decades. However, there still is a considerable gap between the theoretical research and practical applications. It was once considered to be a very hard problem and regarded as one of the ultimate goals of character recognition research. It has four inherent difficulties: (1) There are a large number of Chinese characters. (2) Chinese characters are more complicated than other characters. (3) There are many similar Chinese characters. (4) The shape variation of Chinese characters caused by different writers is very large. Among these difficulties, the shape variation is the most serious problem. However, the shapes of characters written by one person are relatively stable. Based on this consideration, it is expected that a handwritten Chinese character recognition system will perform better if it is at first adapted to the writing style of one person and then recognizes the characters just written by the same person. This is so-called personal or writer-dependent handwritten Chinese character recognition. It was introduced as a feasible way to bring handwritten Chinese character recognition into practical use. The research work described in this dissertation is mainly centered about this topic. After a careful investigation on various methods for the recognition of handwritten Chinese characters, several simple and efficient algorithms are proposed and a personal handwritten Chinese character recognition system is developed. First, a linear size normalization algorithm is proposed, which is especially suitable for personal handwritten Chinese character recognition. It can maintain the relative position of the centroid as well as the ratio of the width and height of a character image, thus highly reflects the personal handwriting characteristics. Its operation is simpler and its performance is better than that of other traditional linear and nonlinear size normalization algorithms. Second, a flexible template matching algorithm for handwritten Chinese character recognition is proposed on the basis of a dynamic model introduced by Burr in 1980. It can deform a temple Chinese character by using the dynamic model so that the template is closer to an unknown character before performing the similarity measure between these two characters. It can also average the images of several samples of a Chinese character to produce an intermediate image as the standard template of this character. Third, based on the consideration that handwritten Chinese characters are complex two-dimensional shapes, a character recognition algorithm using the generalized Hough transform is proposed, in which features extracted from the parameter space are used to calculate the similarity measure between two character images. Thealgorithm is finally implemented in a look-up-table scheme which can be extended to other applications of pattern
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5649
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
李明敬. 特定人手写体汉字识别的方法与系统[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,1995.
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