基于投影法和卷积神经网络的手写汉字图像分割研究

为提高手写汉字的识别率,针对手写汉字的有效分割,建立了卷积神经网络手写汉字体识别模型,并对投影法和轮廓检测法的适用性进行了对比分析。实验结果显示,相较于轮廓检测法,投影法更适用于手写汉字识别中对文字图像的处理工作,可以实现对所需文字的有效切分,同时简化手写汉字识别网络的设置并提高识别准确率。     

国际图像系统（I^2S）上手写汉字识别研究

提出一种用Ｒａｐｉｄ算法对手写汉字进行识别。它是在国际图像系统上实现的。文字图像经摄像机输入，预处理时采用锐化边界，使图像边界清晰，文字图像经正规化后，进行Ｒａｐｉｄ变换，抽取识别特征，特征抽取能比较好地吸收手写汉字变形和扭曲，提高识别率，计算机实验表明，本算法对手写汉字识别是比较有效的。     

用计算机抽出手写汉字的三角号码

本文提出了一种用计算机从手写汉字中抽提出三角号码基本特征的方法,并对抽出的汉字三只角上的特征进行编码,以达到识别分类的目的。从信息论的观点进行研究已经知道,汉字图像的四周对于文字识别的信息量是相对集中的,如果能正确地抽取这些特征,识别就成为可能。具体方法如下:(1)将手写汉字正规化,除去噪音,抽出方向线段,提取特征点的座标及接续关系矩阵。(2)在汉字图像上找出右上、左下、右下三个角上的基本特征点,根据特征点矩阵对每一个字生成三个笔形图像。(3)将笔形图像与三角号码笔形辞典比较,识别抽出三角号码笔形,并加以编码。用计算机模拟实验表明,本方法是有效的。     

汉字财务发票识别技术的研究

本文基于汉字财务发票的特征，论述了设计财务发票识别系统的方法。在垂直分割中，采用了线性整块移动的方式。一种新的算法采用检测和校正后续的倾斜图像。通过半自动装置的研究方式来区分不同的类别(建立局部区域、线段过滤等)，探讨几种形式特征的表示以及构建发票类型特征库。发票类型的识别依据发票的特征，实际的发票形式可与预先印制的空白样张相对照，它需要解决字符和线段类型的粘连问题以及字符的分割和识别问题。基于汉字财务发票图像的特征，本文提出了以识别财务发票的大写数字(汉字)和阿拉伯数字字符的结果为依据的互校装置，以提高识别率。最后为实验结果和结论．     

基于粗糙几何的脱机手写体汉字预处理

传统的图像预处理方法未能预防汉字书写带来的伪特征点（断点、伪交叉点）,利用粗糙几何理论对汉字图像进行粗糙化,克服了以上缺点,同时减少图像大小,提高手写汉字识别速度。     

中文日期词的分割与识别

非限定性手写汉字串的分割与识别是当前字符识别领域中的一个难点问题.针对手写日期的特点,提出了整词识别和定长汉字串分割识别相结合的组合识别方法.整词识别将字符串作为一个整体进行识别,无需复杂的字符串分割过程.在定长汉字串分割过程中,首先通过识别来预测汉字串的长度,然后通过投影和轮廓分析确定候选分割线,最后通过识别选取最优分割路径.这两种分割识别方法通过规则进行组合,大大提高了系统的性能.在真实票据图像上的实验表明了该方法的有效性,分割识别正确率达到了93.3%.     

一种基于SVG的在线连续手写汉字多步分割方法*

基于可伸缩矢量图SVG的在线手写汉字是以SVG图像作为汉字图像格式、以SVG的path对象作为笔画的基本存储单元来对汉字进行显示和存储的,笔画的轮廓是以手写过程中记录的坐标值作为特征数值加以确定的。基于此种SVG手写汉字存储和表示形式,本文提出一种基于图论的在线连续手写汉字多步分割方法。该方法根据汉字笔画间的坐标位置关系对手写笔画序列构建无向图模型,并利用图的广度优先搜索将原笔画序列分割为互不连通的笔画部件,使偏旁部首分离较远、非粘连汉字得到正确分割;然后利用改进的tarjan算法对部件中的粘连字符进行分割,最后基于笔画部件间距,利用二分类迭代算法对间距进行分类,找出全局最佳分割位置,对过分割的部件进行重组合并。实验结果表明,该方法对于在线手写汉字的分割是有效可行的。     

基于笔顺自由及连笔的联机手写汉字识别

提出了一种基于笔顺重排算法的手写汉字识别。将手写汉字的可见线段和不可见线段进行联合编码,并 采用了一种基于单字切分及基本笔顺表的识别方法。首先将单字分解为部件,根据分解的结构,对字典进行粗略的过滤,将字典中不符合待识别汉字拆分结构的字排除,然后根据笔划编码进行识别,有效提高了笔划的匹配速度,较好地解决了联机手写汉字识别中连笔及笔顺自由问题。     

一种手写汉字拓扑图表示及其动态获取

手写汉字的机器识别,属于图像模式分类问题。所谓图像模式分类,指的是把一定范围内的图像,分成预先确定的类别,然后再去对给定范围内的图像进行识别分类。显然,这其中预先确定类属性特征、类标准模板以及具体分类识别策略都是图像模式识别的关键问题。对于我们具体的手写汉字识别问题,由于单字结构分析和基元形态分析一起可以给出汉字形体的完整描述,而统计形态分析却难以满足大规模汉字字集的集群性和分离性要求。     

基于基元模糊关系的手写汉字识别

手写汉字中笔划，部件及其位置关系均产生较大变化，这种变化是引起手写汉字特征不稳定的主要因素。为了减小上述不利影响，使手写汉字特征的描述趋于稳定，本文给出了一种基于汉字基元之间的模糊关系识别手写汉字的方法。     

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     

Prototype learning for structured pattern representation applied to on-line recognition of handwritten Japanese characters

This paper describes prototype learning for structured pattern representation with common subpatterns shared among multiple character prototypes for on-line recognition of handwritten Japanese characters. Prototype learning algorithms have not yet been shown to be useful for structured or hierarchical pattern representation. In this paper, we incorporate cost-free parallel translation to negate the location distributions of subpatterns when they are embedded in character patterns. Moreover, we introduce normalization into a prototype learning algorithm to extract true feature distributions in raw patterns to aggregate distributions of feature points to subpattern prototypes. We show that our proposed method significantly improves structured pattern representation for Japanese on-line character patterns.     

Generating Chinese characters based on stroke splitting and feature extraction

There is a large demand for more fashionable style Chinese characters in advertising, art designing and publishing markets. However, it becomes challenging to create a new font style for so many Chinese characters (over 10,000). To solve this problem, a comprehensive Chinese fonts generating scheme is proposed in this paper. Firstly, a decomposition database for stroke splitting and feature extraction is proposed. Secondly, stroke segmentation rules are defined based on splitting, merging and structural model, location definition and minimum feature extraction. Thirdly, a radical searching algorithm based on stroke splitting is presented. Finally, it is realized that the generated characters can be zoomed, rotated and moved. Experimental result shows that Chinese characters with a new style can be generated rapidly with the proposed scheme. The created characters fit the real ones well with a high fidelity of 96.4%. The usability tests are run and participants’ subjective report show that the performance from the generated characters is similar to the original characters in both recognizability test and style-consistency test. The fonts generating method is also reliable for the other stroke constructed block characters such as Japanese and Korean characters.     

Feature Point Method of Chinese Character Recognition and Its Application

A new method for recognizing Chinese characters is proposed. It is based on the so-called featurepoints of Chinese characters. The feature points we use include those on the stroke of a character, i.e., endpoints, turning points, fork points and cross points, and the key points on the background of character. Thismethod differs from the previous ones for it combines the feature points on stroke with those on back-ground and it uses feature points to recognize Chinese characters directly. A Chinese character recognitionsystem based on top-down dynamical matching of feature point is developed. The system can recognizenot only 6763 printed sample Song font Chinese characters of size 5.6×5.6mm~2 with high recognition rate,but also the general printed books, magazines and documents with a satisfactory recognition rate andspeed.     

进一步的“正易全”——三级汉字编码输入法

本文报告“正易全”汉字输入法的新进展。从整体上来讲,正易全已发展成为全字笔顺、全字笔组和2 21 笔组三级输入法系列。前两级简单灵活,键选率极低,方便大字集查检;第三级在常用字和通用字中表现极佳,适合日常快速打字。在编码技术上,多笔笔组码元的选用、单结构的定义和多结构字的二部划分等方面都作了进一步的简化、系统化和规律化。此外,码表在GB1300011 字符集的基础上增加了1164 个港澳台地区用字或字形。     

Recognition of handwritten Chinese characters by modified Houghtransform techniques

Chinese characters are mapped from the spatial domain into the parametric one for stroke extraction, and the dynamic programming matching (DP matching) algorithm is applied to recognize Chinese characters. This method overcomes not only the problem of noise sensitivity in the local feature approach, but also the problem of being time consuming in the global feature approach. Results are presented for two experiments conducted for a database called ETL8, which contains 881 Chinese characters and 160 variations for each one, to prove the usefulness of the MHT and DP matching methods. An actual recognition rate of 94.5% was obtained for 351 Chinese characters in the ETL8     

Preprocessing for Chinese character recognition and global classification of handwritten Chinese characters

Techniques for calculating the stroke directions of thinned binary characters and for detecting the intersections and end points of strokes by means of pattern matching and weighting method are proposed as a preprocessing of handwritten Chinese character recognition. We also propose a method for global classification of handwritten Chinese characters by means of projection profiles of strokes and show that the method is available for the Chinese characters written in the square style.     

手写体汉字特征分析

本文采用结构方法研究汉字手写体识别。通过对手写体汉字结构特征和书写特点的研究，我们发现，以直划和折划来表征笔划特征，以相交字元和基本字元来表征汉字结构特征，大大降低了结构方法中决策判断串行的程度。     

一种无笔序手写体汉字在线识别方法

文中介绍了一种无笑序手写汉字在线识别方法，提出了基于汉字笔画数的编码方法。整个识别过程为两大部分，首先进行笔画分类，然后根据笔画序列码和汉字的结构完成入手 写何不在于识别。由于本系统要求可以无笔序地进行汉字输入，给汉分类带的来很大的困难，为此，在汉字结构匹配时，提出了一种结构特征搜索及排索算法，很好地解决了要求无笔序输入所带来的问题，实践证明，这种方法用于无笔序手汉字的在线识别是非常有效的。