手写汉字识别的非线性动态部件模板

对以前提出的非线性动态手写模板加以改进并用于手写汉字的部件识别.在训练阶 段,核-主元分析用来捕捉非线性的手写变化.于是,只需改变少量的形状参数就可获得动态变 形的模板.在识别阶段,遗传算法取代了原始的动态通道算法去寻找最优的形状参数.我们对覆 盖2154个汉字类别的200个部件进行了实验,对不用人书写的430,800个测试样本的部件识 别率达97.4%.与现有的代表性部件方法比较也显示本文的方法效果最好.     

Handwritten Chinese radical recognition using nonlinear active shape models

Handwritten Chinese characters can be recognized by first extracting the basic shapes (radicals) of which they are composed. Radicals are described by nonlinear active shape models and optimal parameters found using the chamfer distance transform and a dynamic tunneling algorithm. The radical recognition rate is 96.5 percent correct (writer-independent) on 280,000 characters containing 98 radical classes.     

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.     

面向连续字符识别的手写汉字部件集及统计规律

本文面向手写字符序列输入信号连续识别研究,分析了汉字及联机手写文本的特点,提出并构建了手写汉字部件集。基于该部件集,完成了GB2312-80的6,763个汉字的部件拆分编码和部件集的测试。统计编码数据发现,汉字依手写部件数的分布规律呈对数正态分布。本文从统计学和字符识别技术的角度对手写部件的构字能力作了分析和讨论,部件集的设计方案在部件选择和汉字拆分上均满足设计要求。实验表明,基于手写部件构造的部件识别器对手写汉字和连续汉字的部件识别率分别达到70.21%和58.49%。     

支票手写体汉字大写金额识别的非线性规范化

针对银行支票图像大写金额的无限制手写体汉字识别问题,进行了基于密度均衡原则的非线性规范化研究。提出了一种改进的非线性规范化方法．该方法定义的基于笔画间距和宽度的密度函数,不仅能较好地克服笔画变形的局部性、不规则性,而且能使同一字符内以及不同字符之间的笔画粗细趋于一致;同时,确定了图像中字符的有效区域,并据此改进了基于密度均衡原则的通用表达式,有效地解决了字符整体倾斜和单个笔画比较突出的问题,实验结果表明：该方法比其他同类方法效果更佳,可使银行支票图像的大写金额识别系统的识别正确率提高约1．5％。     

利用双向SOFM网络模拟汉字认知过程的研究

在汉字的认知过程中有“字优效应”和“字劣效应”,前者认为在汉字认知过程中整字信息优于部件或笔画信息,后者反之。以自组织特征映射算法为理论基础,提出了一种双向自组织特征映射(SOFM)网络,利用自组织网络实现根据汉字和部件多维表征的聚类,并建立两层网络之间的连接关系,通过双向测试,得到不同构型汉字所具有的字优效应和字劣效应,从新的角度实现了SOFM的应用。研究结果对于汉字教学方法有一定的参考价值。     

一种连续手写中文的演化分割方法

在连续手写中文中,有偏旁部首离得较远的单字,单字之间可能会存在粘连、重叠。针对这种情况给出了一种基于识别得分提取单字的演化方法。对行笔划序列进行二进制编码,采用改进的遗传算法实现演化过程。染色体中连续0或1对应的笔划组成候选单字。用汉王手写单字识别器获取它们的识别得分,以单字个数较少和总的识别得分较大为优化目标。遗传算法中的变异概率和交叉概率自适应生成。测试结果表明该方法对连续手写中文具有较好的分割效果。     

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.     

A Nom historical document recognition system for digital archiving

A Nom historical document recognition system is being developed for digital archiving that uses image binarization, character segmentation, and character recognition. It incorporates two versions of off-line character recognition: one for automatic recognition of scanned and segmented character patterns (7660 categories) and the other for user handwritten input (32,695 categories). This separation is used since including less frequently appearing categories in automatic recognition increases the misrecognition rate without reliable statistics on the Nom language. Moreover, a user must be able to check the results and identify the correct categories from an extended set of categories, and a user can input characters by hand. Both versions use the same recognition method, but they are trained using different sets of training patterns. Recursive X–Y cut and Voronoi diagrams are used for segmentation; k–d tree and generalized learning vector quantization are used for coarse classification; and the modified quadratic discriminant function is used for fine classification. The system provides an interface through which a user can check the results, change binarization methods, rectify segmentation, and input correct character categories by hand. Evaluation done using a limited number of Nom historical documents after providing ground truths for them showed that the two stages of recognition along with user checking and correction improved the recognition results significantly.     

基于实时样本采集的个性化手写汉字输入系统设计

手写汉字识别是手写汉字输入的基础。目前智能设备中的手写汉字输入法无法根据用户的汉字书写习惯,动态调整识别模型以提升手写汉字的正确识别率。通过对最新深度学习算法及训练模型的研究,提出了一种基于用户手写汉字样本实时采集的个性化手写汉字输入系统的设计方法。该方法将采集用户的手写汉字作为增量样本,通过对服务器端训练生成的手写汉字识别模型的再次训练,使识别模型能够更好地适应该用户的书写习惯,提升手写汉字输入系统的识别率。最后,在该理论方法的基础上,结合新设计的深度残差网络,进行了手写汉字识别的对比实验。实验结果显示,通过引入实时采集样本的再次训练,手写汉字识别模型的识别率有较大幅度的提升,能够更有效的满足用户在智能设备端对手写汉字输入系统的使用需求。     

一种脱机手写体汉字识别的容错编码方法研究

手写体汉字识别是字符识别领域中的难点。为了使机器识别汉字适应于手写体汉字的变形等因素,基于人类认识汉字的容错机理,提出了一种用于机器识字的汉字容错编码方法,以提高手写体汉字识别率。该编码方法首先对横竖撇捺笔划形态给出了模糊化表示;然后定义了仿人拆字的字元集,并给出了易混淆笔划字元的多归类容错编码;接着给出了笔划字元的顺序判断规则和归结了36类简单常用字的部首子结构,并给出冗余的容错编码;进而建立了仿人构字的汉字编码规则和具有容错性的多模板字典,并对《新华字典》中收录的10000余个单字汉字进行了标准编码,重码率为0.48%;最后对HCCORG和NKIM手写体汉字库中的100个手写体汉字进行了仿真识别,识别正确率为96%。试验结果表明,这种编码方法可生成多模板字典,不仅对手写体汉字变形具有较好的容错性,且重码率和误识率较低。     

基于模糊规则和相似测量的手写汉字预分类法

文章提出了一种手写汉字预分类的新方法,该方法分两步进行,首先提取笔划密度特征并用模糊规则产生四个预分类组;然后通过模糊逻辑处理将各组字符分别转换成基于非线性加权函数的模糊样板并通过基于模糊相似测量的匹配算法、相似性测量样板的分级分类进行预分类。测试结果表明,该方法效果良好,预分类正确率达到98.17%。     

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.     

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.     

Handwritten Chinese Character Recognition Based on Primitive and Fuzzy Features via the SEART Neural Net Model

A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach.     

基于卷积神经网络的车牌字符识别

车牌字符识别是智能车牌识别系统中的重要组成部分。针对车牌字符类别多、背景复杂影响正确识别率的问题,提出了一种基于卷积神经网络（CNN）的车牌字符识别方法。首先对车牌字符图像进行大小归一化、去噪、二值化、细化、字符区域居中等预处理,去除复杂背景,得到简单的字符形状结构;然后,利用所提出的CNN模型对预处理后的车牌字符集进行训练、识别。实验结果表明,所提方法能够达到99.96%的正确识别率,优于其他三种对比方法。说明所提出的CNN方法对车牌字符具有很好的识别性能,能满足实际应用需求。     

基于神经网络和模糊匹配算法的手写汉字预分类研究

对手写汉字识别问题,提出了一种在识别之前对手写汉字预分类的新方法,该方法用Neocognitron网提取字符笔画特征,然后采用有监督的扩展ART神经网络（SEART）产生一定数量的预分类组并通过基于模糊相似测量的匹配算法进行预分类。实验表明,该方法用于手写汉字分类效果良好,预分类正确率达到98.22％。     

汉字笔段形成规律及其提取方法

该文从点阵图像行(列)连通像素段出发，研究汉字图像的笔段构成，发现汉字点阵图像仅由阶梯型笔段和平行长笔段两种类型的笔段构成，并归纳出阶梯型笔段和平行长笔段的形成规律．以笔段形成规律为基础提出了汉字笔段的提取方法，该方法将像素级汉字图像转变为以笔段为单位的图像，有利于汉字识别、汉字细化及汉字字体的自动生成．最后该文给出了印刷体和手写体汉字笔段提取的实验结果．     

技术综合集成在模式识别中的应用

本文介绍了采用综合技术集成的方法，解决印刷汉字识别系统误识率太高的重大难题，并通过集成系统的实践，证实了其技术集成优势，由于识别方法的互补效应，不仅提高了识别的正确率，而且使误识率得到大幅度的降低，采用该集成办法研制的系统，经过１００万字的实际文章的测试，系统的识别率超过９８％，误识率小于０．２％，尤其是汉字的误识率小于０．１％。