基于不对称性的相似汉字识别方法

相似字识别的正确与否对整个识别系统的准确性和可用性都有着极大的影响。在实际应用中,我们发现相似汉字之间的误识存在不对称性,并对这种不对称现象的成因进行了细致的探讨和分析。基于这种不对称性,本文提出了一种分类的部分空间方法来解决相似字的识别问题。相似字按其结构特点被分成若干基本类别,不同类别在相应的部分空间提取不同的特征进行比较,以达到正确识别相似字的目的。实验结果表明了本方法的有效性,相似字识别的准确性得到了很大的提高,其中易错相似字的识别正确率平均提高了4.55个百分点,不易错相似字的识别正确率平均提高了0.38个百分点。     

基于组件合并的手写体汉字串分割

人们对孤立的手写体汉字字符的离线 识别做了大量的研究工作,而走向实用化的进展并不快.除了单字识别率不理想以外,从文本 中正确分割出单个汉字字符也是一个主要难题,因为字符的识别离不开正确分割.利用汉字的 基本结构特征,根据两个组件之间的上下、左右和包围关系,对组件进行合并形成完整的汉字 图像.对整个汉字字符串中组件的宽度和相邻组件的间距进行分析,有助于左右关系组件的合 并.实验结果表明,该方法对手写体汉字字符串具有理想的分割效果.     

基于Rough集理论的车牌汉字特征提取

车牌首位汉字特征提取和识别是一个难点。传统的车牌汉字的特征提取方法是在具有先验知识的情况下进行的,先验知识的好坏对结果有着非常重要的影响。Rough集,理论上可以从数据集中直接提取特征,不依靠先验知识。先用Rough集理论提取待识别汉字的特征,再用这些特征进行模板匹配。实验结果表明该方法有比较好的识别效果。     

基于Rough集理论的车牌汉字特征提取

车牌首位汉字特征提取和识别是一个难点。传统的车牌汉字的特征提取方法是在具有先验知识的情况下进行的,先验知识的好坏对结果有着非常重要的影响。Rough集,理论上可以从数据集中直接提取特征,不依靠先验知识。先用Rough集理论提取待识别汉字的特征,再用这些特征进行模板匹配。实验结果表明该方法有比较好的识别效果。     

基于统计部首模型的联机手写汉字识别方法

利用汉字的部首层次结构有助于减小字符识别器的存储空间和提高泛化性、适应性,但部首分割一直是一个难点.提出一种新的基于部首的联机手写汉字识别方法,该方法把部首形状信息和几何信息集成到识别框架中,在组合搜索过程中利用字符-部首的层次结构字典引导部首的分割与识别,从而提高部首分割的准确率.为克服部首间的连笔,引入角点检测提取子笔划.部首识别采用统计分类器,模型参数通过自学习得到.在字符识别中,采用了2种不同的字典表示以及相应的不同搜索算法.该方法已用于左右与上下结构的字符集,实验结果表明了该方法的有效性.     

Chinese character recognition: history, status and prospects

Chinese character recognition (CCR) is an important branch of pattern recognition. It was considered as an extremely difficult problem due to the very large number of categories, complicated structures, similarity between characters, and the variability of fonts or writing styles. Because of its unique technical challenges and great social needs, the last four decades witnessed the intensive research in this field and a rapid increase of successful applications. However, higher recognition performance is continuously needed to improve the existing applications and to exploit new applications. This paper first provides an overview of Chinese character recognition and the properties of Chinese characters. Some important methods and successful results in the history of Chinese character recognition are then summarized. As for classification methods, this article pays special attention to the syntactic-semantic approach for online Chinese character recognition, as well as the metasynthesis approach for discipline crossing. Finally, the remaining problems and the possible solutions are discussed.     

On-line recognition of handprinted Kanji characters

Difficulties in Kanji (Chinese character) recognition stem from its large character set (about 5000 characters) and the large number of strokes (up to about sixty) in each character.

The paper describes a preliminary approach to this Kanji recognition problem. In the present method, a handprinted Kanji character is coded into a symbol string using the binary relation between stroke and reference zone. Two symbol string recognition methods are proposed and investigated; the direct matching recognition (DMR) method and the unit structure recognition (USR) method.

The DMR method worked efficiently for characters which have up to five strokes. The USR method represents Kanji characters with a structural unit combination. This method worked efficiently for multi-stroke characters and greatly reduced dictionary update labor.     

基于语义的手写汉字识别模型

目前,已经有很多文献阐述了不同的手写汉字识别算法,但是绝大多数算法都是针对单个汉字进行识别的,所以对于比较容易混淆的字,它们的识别效果都不好。该文针对这个问题,在单个汉字识别的基础上,结合汉语字典,加入了对前后汉字的语义考虑,大大地提高了这些容易混淆的汉字的识别率。     

Chinese character recognition :history ,status and prospects

Chinese character recognition (CCR) is an important branch of pattern recognition. It was considered as an extremely difficult problem due to the very large number of categories, complicated structures, similarity between characters, and the variability of fonts or writing styles. Because of its unique technical challenges and great social needs, the last four decades witnessed the intensive research in this field and a rapid increase of successful applications. However, higher recognition performance is continuously needed to improve the existing applications and to exploit new applications. This paper first provides an overview of Chinese character recognition and the properties of Chinese characters. Some important methods and successful results in the history of Chinese character recognition are then summarized. As for classification methods, this article pays special attention to the syntactic-semantic approach for online Chinese character recognition, as well as the metasynthesis approach for discipline crossing. Finally, the remaining problems and the possible solutions are discussed.     

一个实用的古籍印刷汉字识别系统

本文采用Shannon理论, 讨论了古籍印刷汉字识别字域地选择所受的约束,汉字特征提取的性能限度, 以及如何用汉字的统计特性, 进一步提高来统的识别率在理论分析的基础上, 经过大量实验研究, 所完成的古籍印, 汉字识别系统对已标注过720万字的古籍录入显示了它的优越性能。     

基于嵌入式平台的手写汉字识别系统的设计

随着移动技术与相关技术的迅速发展,手机、个人掌上电脑(PDA)、笔记本电脑等各种电子设备变得流行,它们已成为人们工作和娱乐必不可少的随身用品。对于各种移动电子设备在中国的推广使用,汉字输入是一个必须考虑的问题。传统的输入方式大多使用键盘,不论是笔记本电脑使用的标准键盘,还是各手机厂商设计的简化键盘,都是使用键盘采集信息,然后通过汉语拼音或者笔画输入等方式完成汉字输入。对于嵌入式小型设备来说,原有键盘设计引起占用空间大和输入汉字效率低等诸多问题。如何解决这些问题,同时保证设备足够的显示空间,又不添加新的复杂硬件设备。一种叫做触摸屏手写汉字输入的技术越来越受到人们的推崇。以Windows CE 5.0为运行平台,Embedded Visual C 4.0,为开发环境,设计和实现了一套屏幕手写识别系统,不仅能对现有汉字进行有效识别,用户还可以根据需要自行对字库扩展,有助于提高汉字的识别率。     

基于嵌入式平台的手写汉字识别系统的设计

随着移动技术与相关技术的迅速发展,手机、个人掌上电脑（PDA）、笔记本电脑等各种电子设备变得流行．它们已成为人们工作和娱乐必不可少的随身用品。对于各种移动电子设备在中国的推广使用．汉字输入是一个必须考虑的问题。传统的输入方式大多使用键盘,不论是笔记本电脑使用的标准键盘,还是各手机厂商设计的简化键盘,都是使用键盘采集信息,然后通过汉语拼音或者笔画输入等方式完成汉字输入。对于嵌入式小型设备来说．原有键盘设计引起占用空间大和输入汉字效率低等诸多问题。如何解决这些问题,同时保证设备足够的显示空间,又不添加新的复杂硬件设备。一种叫做触摸屏手写汉字输入的技术越来越受到人们的推崇。以Windows CE5．0为运行平台,Embedded Visual C＋＋ 4．0,为开发环境,设计和实现了一套屏幕手写识别系统,不仅能对现有汉字进行有效识别．用户还可以根据需要自行对字库扩展．有助于提高汉字的识别率．     

Adaptive recognition of Chinese characters: imitation ofpsychological process in machine recognition

This paper is focused on imitation of human psychological process in machine recognition of Chinese characters. Some results of research on human Chinese character recognition have been discussed and unified into a compound mechanism with an adaptive and self-developing nature. A machine imitation model has been proposed for Chinese character recognition with different routines. By some simplification but with the crucial feature of the model being retained, an experimental system for handprinted Chinese character recognition based on the novel concept has been built. Experimental results have shown that the associated routines continuously improve their performance during their work even after supervised training is halted. The routine of the global pattern approach eventually learns most of the classes and the recognition process gradually shifts from the subpattern approach to the global pattern approach     

基于手写体汉字笔画提取重构的识别方法

提出了手写体汉字笔画宽度提取、基于提取出的笔画宽度归一化手写体汉字的方法，给出手写体汉字笔画重构的思想，实现了一种基于手写体汉字笔画提取的汉字重构并最终识别手写体汉字的算法，构建了手写体汉字的识别系统。实验证实，该方法可保证原有笔画特征信息，且能有效地识别手写体汉字。     

卷积深度神经网络的手写汉字识别系统

针对传统两级手写汉字识别系统中手写汉字识别的特征提取方法的限制问题,提出了一种采用卷积神经网对相似汉字自动学习有效特征进行识别的系统方法。该方法采用来自手写云平台上的大数据来训练模型,基于频度统计生成相似子集,进一步提高识别率。实验表明,相对于传统的基于梯度特征的支持向量机和最近邻分类器方法,该方法的识别率有一定的提高。     

Recognition of handwritten Chinese characters by critical region analysis

The problem of recognizing offline handwritten Chinese characters has been investigated extensively. One difficulty is due to the existence of characters with very similar shapes. In this paper, we propose a “critical region analysis” technique which highlights the critical regions that distinguish one character from another similar character. The critical regions are identified automatically based on the output of the Fisher's discriminant. Additional features are extracted from these regions and contribute to the recognition process. By incorporating this technique into the character recognition system, a record high recognition rate of 99.53% on the ETL-9B database is obtained.     

On-line handwritten Chinese character recognition via a fuzzy attribute representation

Chinese characters are constructed by strokes according to structural rules. Therefore, the geometric configurations of characters are important features for character recognition. In handwritten characters, stroke shapes and their spatial relations may vary to some extent. The attribute value of a structural identification is then a fuzzy quantity rather than a binary quantity. Recognizing these facts, we propose a fuzzy attribute representation (FAR) to describe the structural features of handwritten Chinese characters for an on-line Chinese character recognition (OLCCR) system. With a FAR. a fuzzy attribute graph for each handwritten character is created, and the character recognition process is thus transformed into a simple graph matching problem. This character representation and our proposed recognition method allow us to relax the constraints on stroke order and stroke connection. The graph model provides a generalized character representation that can easily incorporate newly added characters into an OLCCR system with an automatic learning capability. The fuzzy representation can describe the degree of structural deformation in handwritten characters. The character matching algorithm is designed to tolerate structural deformations to some extent. Therefore, even input characters with deformations can be recognized correctly once the reference dictionary of the recognition system has been trained using a few representative learning samples. Experimental results are provided to show the effectiveness of the proposed method.