基于背景分析的手写数字切分方法

考虑到手写数字串的结构特点,提出一种以分析特定背景区域为主,针对具体粘连数字采用不同分割策略的切分方法。文中引入蓄水池的概念来形象描绘出背景区域中字符间的粘连部分,并从中抽取某些特征对字符的具体粘连形态进行了归纳分类。在分割过程中,根据字符的粘连类型选用不同的滴水算法来求得分割路径。实验结果表明了该方法对于手写数字分割的有效性。     

基于字符边缘特征的手写账号切分算法

结合银行票据自动识别系统(Bank-OCR)的开发研究。提出了基于手写数字串边缘特征的手写数字串的切分算法。文章分析了手写数字串的切分难点，提出了数字账号边缘特征的概念，并在利用下边缘特征判断连体字符个数的基础上。利用上下边缘中波峰波谷的特征来对连写数字进行有效切分。实验结果表明对于非限制自由手写数字串的最终切分正切率达到863％，满足了银行票据的预处理要求。     

基于笔划组合的手写数字切分

主曲线是一种新的基于非线性变换的特征抽取方法,它是一种通过数据分布“中间”并满足“自相合”的光滑曲线来进行特征提取的方法。为了提高手写数字串切分的正确率,提出了一种基于笔划组合的手写数字串切分方法。该方法首先使用主曲线完成字符模板的笔划抽取,然后以字符识别器提供的置信度为依据来组合笔划,以实现手写数字串的切分过程。另外,在字符识别器设计方面,则是使用基于数字轮廓分段特征与规范化模板特征这两个单特征分类器组合。实验表明,分别基于这两个特征的分类器具有较强的互补性。由于字符识别器的置信度难以真实反映识别结果,为此需使用类条件置信变换法,通过估计分类器的后验概率来对识别器的置信度进行修正。实验结果表明,该方法对于手写数字的分割是有效的。     

基于候选特征笔画和多类阈值的手写汉字切分

通过分析汉字的常见结构,鉴于汉字与汉字之间的距离和构成汉字的部件之间的距离的显著差异性,提出一种基于候选特征笔画和多类阈值的手写汉字切分方法.首先从构成手写汉字的笔画集合中提取候选特征笔画,根据候选特征笔画将手写汉字预切分,然后利用基于间距阈值的部件组合规则对过切分的汉字部件进行组合,最后采用基于单字宽度阈值的粘连汉字判断规则搜索粘连汉字,对粘连汉字进行递归切分.实验表明,该方法对连续手写汉字的切分准确率较高,具有一定的实用性.     

粘连字符切分综述

字符切分是字符识别系统的一个重要环节。粘连则是指字符图像问有笔画接触的情况，它普遍存在于手写和低质量的印刷文档中。然而普通切分算法很难处理粘连字符。本文综述了主要的粘连字符切分算法以及切分路径的评价方法，在详细介绍算法流程的基础上，对每种算法的优缺点以及粘连字符切分的未来工作进行了探讨。     

基于模具的手写数字串切分算法及其应用

提出了一种基于模具的手写数字串切分算法,该算法通过总结手写数字串中字符之间的连接特点,归纳出一套合理的切分曲线类型,并根据这些曲线类型设计出多种切分模具,从而将字符的切分过程变为各种模具的试用和优选过程．通过在银行票据OCR系统中的应用,验证了算法的有效性．     

基于凹凸特性的非限制粘连手写数字串切分

针对现有的切分算法结构复杂,时间和空间复杂度高等不足,提出了一种基于凹凸特性的非限制粘连手写数字串切分的新方法。首先计算数字串图像的赋值背景,然后从中提取凹凸特性,找到切分区域,最后在切分区域内提取切分线。该方法简单快速,在提高切分正确率的同时也降低了复杂度。利用NISTSD19收集到的样本进行实验,正确率高达97.5%,切分时间也大大缩短。     

联机手写笔画特征抽取的逼近-合并算法

为了对联机手写字符识别的笔画进行精确描述,提出了一种基于字符笔画特征抽取的＂逼近-合并＂算法.该算法分析了字符笔画的多边形逼近,求出偏离度最小的多边形逼近,并对该多边形的边进行合并,抽取出笔画方向码,实现了联机手写字符笔画的更有效合并.该方法应用在联机手写体字符识别实验系统中,其识别率为99.13%.     

一种基于像素点的手写体文本图像分割方法

文字的正确识别与否很大程度上取决于能否对文字进行有效的分割。在分析传统手写文字文本图像切分算法的基础上,提出一种基于像素点的文字分割方法。算法具有很强的抗干扰性,能够有效地解决手写过程中的小角度歪斜以及字符笔画断裂和粘连的问题。通过MATLAB的仿真试验,结果表明此方法能够对手写过程中普遍存在歪斜的文字进行有效的分割。     

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。     

Segmentation of single- or multiple-touching handwritten numeral string using background and foreground analysis

An approach of segmenting a single- or multiple-touching handwritten numeral string (two-digits) is proposed. Most algorithms for segmenting connected digits mainly focus on the analysis of foreground pixels. Some concentrated on the analysis of background pixels only and others are based on a recognizer. We combine background and foreground analysis to segment single- or multiple-touching handwritten numeral strings. Thinning of both foreground and background regions are first processed on the image of connected numeral strings and the feature points on foreground and background skeletons are extracted. Several possible segmentation paths are then constructed and useless strokes are removed. Finally, the parameters of geometric properties of each possible segmentation paths are determined and these parameters are analyzed by the mixture Gaussian probability function to decide the best segmentation path or reject it. Experimental results on NIST special database 19 (an update of NIST special database 3) and some other images collected by ourselves show that our algorithm can get a correct rate of 96 percent with rejection rate of 7.8 percent, which compares favorably with those reported in the literature.     

一种无约束手写体数字串分割方法

针对无约束手写体数字串中的连笔字符,本文提出以基于识别的分割方法为主,结合运用剖分方法和全局识别方法等多种分割策略的数字串分割方法。这种方法直接针对数字串分割,也可以运用到非数字字符串的分割中,其分割思想对连笔汉字的分割也具有一定指导意义。     

基于Viterbi 算法的粘连断裂印刷体数字行切分识别方法

粘连断裂字符行的切分识别，是很多OCR 实际应用中存在的主要困难之一. 本文针对粘连断裂的印刷体数字行，提出了一种基于Viterbi 算法的切分识别方案，该方案采用两次切分识别的层次型结构. 在第二次切分识别过程中，首先，在候选切分点区域，结合灰度图像与二值轮廓信息，采用基于Viterbi 算法搜索的非直线路径进行切分，得到有效的切分路径；然后，结合分类器输出的可信度，采用Viterbi 算法来合并前面得到的候选切分图像块，进行动态切分与识别. 实际的金融票据识别系统实验表明，本文提出的印刷体数字行切分识别方法能够较好的克服字符行的粘连与断裂情况，提高了识别系统的识别率和鲁棒性.     

A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

For the first time, a genetic framework using contextual knowledge is proposed for segmentation and recognition of unconstrained handwritten numeral strings. New algorithms have been developed to locate feature points on the string image, and to generate possible segmentation hypotheses. A genetic representation scheme is utilized to show the space of all segmentation hypotheses (chromosomes). For the evaluation of segmentation hypotheses, a novel evaluation scheme is introduced, in order to improve the outlier resistance of the system. Our genetic algorithm tries to search and evolve the population of segmentation hypotheses, and to find the one with the highest segmentation/recognition confidence. The NIST NSTRING SD19 and CENPARMI databases were used to evaluate the performance of our proposed method. Our experiments showed that proper use of contextual knowledge in segmentation, evaluation and search greatly improves the overall performance of the system. On average, our system was able to obtain correct recognition rates of 95.28% and 96.42% on handwritten numeral strings using neural network and support vector classifiers, respectively. These results compare favorably with the ones reported in the literature.     

Context-directed segmentation algorithm for handwritten numeral strings

A context-directed algorithm is proposed for segmenting connected numeral strings into their components. The algorithm is hierarchical (tree-like structure) in the sense that it tests various hypotheses ranging from the case where the numerals are completely isolated to that where the numerals may be connected, touching and/or existing in overlapping fields. Test results indicate that the algorithm is very effective in providing an accurate segmentation in a form suitable for further processing by a recognition algorithm.     

提高汉语自动分词精度的多步处理策略

汉语自动分词在面向大规模真实文本进行分词时仍然存在很多困难。其中两个关键问题是未登录词的识别和切分歧义的消除。本文描述了一种旨在降低分词难度和提高分词精度的多步处理策略,整个处理步骤包括7个部分,即消除伪歧义、句子的全切分、部分确定性切分、数词串处理、重叠词处理、基于统计的未登录词识别以及使用词性信息消除切分歧义的一体化处理。开放测试结果表明分词精确率可达98%以上。     

Two-stage segmentation of unconstrained handwritten Chinese characters

Correct segmentation of handwritten Chinese characters is crucial to their successful recognition. However, due to many difficulties involved, little work has been reported in this area. In this paper, a two-stage approach is presented to segment unconstrained handwritten Chinese characters. A handwritten Chinese character string is first coarsely segmented according to the background skeleton and vertical projection after a proper image preprocessing. With several geometric features, all possible segmentation paths are evaluated by using the fuzzy decision rules learned from examples. As a result, unsuitable segmentation paths are discarded. In the fine segmentation stage that follows, the strokes that may contain segmentation points are first identified. The feature points are then extracted from candidate strokes and taken as segmentation point candidates through each of which a segmentation path may be formed. The geometric features similar to the coarse segmentation stage are used and corresponding fuzzy decision rules are generated to evaluate fine segmentation paths. Experimental results on 1000 Chinese character strings from postal mail show that our approach can achieve a reasonable good overall accuracy in segmenting unconstrained handwritten Chinese characters.     

基于多分类器的无分割手写数字字符串识别算法

手写体数字字符串识别常用于邮件自动分拣、银行票据和财务报表的录入中,针对其分割识别算法复杂度较高、准确率较低的问题,提出一种多分类器下无分割手写数字字符串识别算法。该算法的核心是采用四个分类器实现粘连字符串的无分割识别;将残差结构应用于LeNet-5网络,以增加网络深度,提高识别准确率,加快收敛速度;使用动态选择策略,以避免长度分类器误分类对识别结果的影响。实验结果表明,在NIST SD19一位数字和Synthetic数据集训练网络下,使用NIST SD19上长度为2、3、4、5、6的字符串验证网络,其识别准确率分别为99.3%、98.5%、98.1%、96.6%和97.2%。