快速精确识别车牌字符的方法

提出了一种利用车牌字符集特征来优化字符骨骼处理、改进骨骼变长编码的新方法,并把其用于车牌字符精确识别.该方法先利用车牌字符集特征优化处理标准车牌字符骨骼,再将"替换规则"与8方向链码相结合对其进行变长编码的结果作为模板,然后用同样方法得到待识别的车牌字符编码,将该编码与模板进行最佳匹配,实验结果表明,该方法具有实现简单、处理数据量小、抗旋转和对低分辨率车牌字符识别性能好等优点.     

融合LeNet-5改进的扫描文档手写日期识别

研究LeNet-5在扫描文档中手写体日期字符识别的应用,由于文档扫描的过程中会引入各种噪声,特别是光照和颜色干扰,直接使用LeNet-5算法不能取得较好效果。先在整份文档中对特定待识别字符的进行定位和划分,并对划分出的字符图像进行去噪、灰度化和二值化处理等预处理,接着将字符图像分割成一个个单个字符,然后在LeNet-5网络基础上结合模型匹配法实现对手写体日期字符的识别。分析在不同参数组合下的识别效果,调整算法模型参数有效地提升了模型对于实际对象的性能,实现出一种能够对手写体日期字符集实现较好识别效果的算法。实验结果表明了算法的有效性,并应用于具体工程实践。     

改进神经网络算法在车牌识别中的应用

研究现代智能交通管理中的车牌准确识别问题.由于车牌图像存在模糊不清、倾斜,分割后字符图像笔画粗细不均、断续不完整等特殊性,导致传统车牌识别算法识别速度慢、识别正确率低,不能适应车牌识别的实时性要求.为了提高车牌识别正确率,提出一种BP神经网络的车牌识别算法.该算法首先对车牌字符图像进行归一化处理,消除图像中无用信息,然后对车牌字符进行特征提取,消除笔画粗细不均、断续不完整等影响,再将提取车牌字符特征输入到BP神经网络进行学习和识别,并采用动量因子和自适应学习速率对BP神经网络进行改进,加快其收敛速度,从而提高识别的实时性.仿真结果表明,改进用BP神经网络提高了车牌识别正确率和识别速度,缩短了识别时间,适合于实时性强的智能交通管理系统应用.     

基于字符特征的车牌英文和数字字符自联想识别

汽车牌照字符的准确识别是汽车牌照实时自动识别技术中的关键技术之一.本文提出采用自联想神经网络对字符进行识别.针对离散Hopfield网络记忆容量小的缺陷,本文首先提出采用一种两层反馈神经网络,其吸引子的吸引半径和网络的稳定状态都有明显的改善,使网络的记忆容量和容错性能都得到大幅度地提高;然后在仔细分析车辆牌照中英文和数字字符结构特点的基础上,以闭合曲线、三叉点和四叉点为结构特征将字符划分为多个子集,使网络针对各子集的容错性有了进一步地显著提高;最后通过实验对比不同算法下字符的识别率,证明文中算法在车牌字符识别中的准确性和快速性.     

一种基于前端预测识别的粘连字符分割方法

在字符识别领域,对粘连字符的识别是一个被广泛关注的技术难点,未能准确分割粘连字符是产生识别错误的主要原因之一。在总结已有方法的特点及不足的基础上,提出了基于前端预测识别来分割粘连字符的方法。首先根据粘连字符图像的特征初步确定前端字符的候选字符集,并通过验证候选字符与前端字符图像匹配的必要条件进一步对其筛选,然后使用候选字符的屏蔽码自适应地提取前端字符图像,最后由分类器对提取结果加以验证,达到分割和识别粘连字符的目的。该方法可以适应多种类型的粘连字符,准确性高,且在字符图像质量较差时具有较强的鲁棒性。     

基于特征补偿的视频字幕识别研究

视频中的字幕对多媒体检索技术提供了重要的信息,主要介绍视频字幕识别技术中的特征提取方法.根据字幕不同于印刷体和手写体中常出现的问题,提出了字符位置特征和字符区域笔画分布特征、字符笔画密度特征两种修改过的特征提取方法与传统的字符外围特征结合使用,并进行了实验测试,结果表明这些方法对字幕识别是可行的.     

基于弹性笔画长度的汉字笔画平面抽取及识别

针对手写体汉字识别过程中的特征抽取，提出了一种改进的抽取笔画平面的方法。首先，将手写汉字图像进行非线性规一化；然后，利用弹性笔画长度，根据汉字的横竖撇捺四种笔画分别抽取出四种笔画的平面；最后，将四个笔画平面分别均匀划分成S×S个小网格，在每个小格内计算其笔画交叉数目，便得到了一个4S2维的特征向量。实验利用基于最小距离的分类器对含有7600（19类）个汉字的测试集进行了分类，取得了较好的识别效果。     

一种基于骨架特征和神经网络的手写体字符识别技术

对英文字符集的识别问题,提出了一种依据细化后的字符骨架特征进行分类,并对每一类别的字符各自建立神经网络进行训练和识别的方案。在二值图象理论的基础上,通过对字符轮廓特征的分析,提出了一种对英文字符集的分类方案,同时提出了一种快速有效的骨架链码形成和特征提取算法。     

编码字符集中子集的完整性

本文提出编码字符集中的完整性问题。（一）中给出两类编码实例。一类严格遵从“一个字符只分配给一个码位”或“任何字符都不重复分配码位”（ 简称一符一码）的节约原则。另一类以一符两码可多码的方式, 以码位的牺牲换取子集的完整性。（二）中解释了子集完整性概念和意义。说明了有意义的子集通常是现实中某子系统的反映。多文种编码字符集的许多子集往往与某自然语言系统相关联,这种子集的完整性, 也就与相应语言文字的系统性相关联。据此提出了完整性条件, 此较了完整性得失, 说明了完整性的某种相对性。（三）中指出不少字符集, 含ISO 10646 DP版及DIS版, 的一符一码原则损害了若干子集的完整性。在那里,拉丁文字圈中除英文以外的各国家、各民族的文字字符子集大多被肢解了, 只有字母表是英文字母表（含元素2X26=52个）子集者例外。斯拉夫文字圈、阿拉伯文字圈情况相似。文中指出汉语拼音字母子集, 无论在汉字编码的中国国家标准中还是国际标准10646中放严重肢解了。 本文是作者另文《字符集的序性》〔9〕的续编。本文的讨论仍除外汉文及藏文     

主成分分析方法(PCA)在车辆牌照识别中的应用

将主成分分析方法（PCA）应用于车牌识别。首先根据采集到样本分类构造各类样本对应特征子空间,然后对待识别字符图片进行预处理,再分别向各类特征空间投影,根据重构误差判断类别识别字符。     

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.     

一种现代藏文笔段提取算法

针对藏文字符笔段的几何特征和拓扑结构,本文提出了一种基于字符轮廓信息的藏文笔段提取算法:通过链码跟踪的方法得到笔段轮廓的点列,然后从点列中提取特征点并利用特征点切分出笔段,最后用笔段的轮廓线代替骨架线来表征藏文的笔段。本算法用于印刷体藏文笔段提取,取得了良好的效果,避免了传统细化算法所造成的畸变,提高了笔段提取的抗干扰能力,并减小了计算量,加快了特征提取的速度。     

Iconic and multi-stroke gesture recognition

Many handwritten gestures, characters, and symbols comprise multiple pendown strokes separated by penup strokes. In this paper, a large number of features known from the literature are explored for the recognition of such multi-stroke gestures. Features are computed from a global gesture shape. From its constituent strokes, the mean and standard deviation of each feature are computed. We show that using these new stroke-based features, significant improvements in classification accuracy can be obtained between 10% and 50% compared to global feature representations. These results are consistent over four different databases, containing iconic pen gestures, handwritten symbols, and upper-case characters. Compared to two other multi-stroke recognition techniques, improvements between 25% and 39% are achieved, averaged over all four databases.     

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。     

基于小波变换的图像汉字笔划特征提取方法

借鉴仿生模式识别的认知观点,从汉字的构造机理和人类认识汉字的习惯角度出发,提出一种基于小波变换的图像汉字识别方法。制定了图像汉字笔划特征提取的具体规则,采用小波变换的方法对图像汉字边缘和笔划轮廓进行检测,通过有效提取图像汉字笔段信息,进行笔段合成,生成汉字或汉字的基本笔划。仿真实验结果表明,这种方法提高了图像汉字笔划特征提取的准确率和稳定性,对于印刷体和书写较规范的手写体图像汉字具有极高的识别率。     

基于多模板归一化的维吾尔文字母识别算法

该文针对手写维文字符识别中字符宽高比变化剧烈,单一模板归一化后提取字符特征,不能有效增加异类字符之间的差异性,提出了针对维文字形特点的多模板归一化算法。训练阶段,由多模板归一化字符图像,提取特征并训练对应分类器;识别阶段,用主笔画散度方向作为维文字形参数, 对不同字形选用最优模板进行归一化处理后提取特征,并送入该模板对应的分类器。多模版归一化有效利用了手写维文字符字形特征,克服了单模板归一化时异类维文字符差异减小的不利影响。实验结果表明多模板归一化算法较单模板归一化算法在识别性能上有所提高。     

基于连笔消除的空间手写字符识别方法

信息的连续采集会造成部分字符存在连笔,进而影响字符识别率.为此,提出一种基于连笔消除的空间手写字符识别方法.将空间手写字符平面化,提取字符拐点和笔画方向特征.为避免笔画的误消除,利用支持向量机把未知字符分为带连笔字符和非连笔字符,通过连笔的书写特征消除连笔,将空间字符轨迹转化为平面字符轨迹,直接用平面字符分类器进行字符识别.实验结果表明,该方法连笔消除效果显著,利用现有字符库即可获得较高的字符识别率.     

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.     

A method of recognition of arabic cursive handwriting

In spite of the progress of machine recognition techniques of Latin, Kana, and Chinese characters over the two past decades, the machine recognition of Arabic characters has remained almost untouched. In this correspondence, a structural recognition method of Arabic cursively handwritten words is proposed. In this method, words are first segmented into strokes. Those strokes are then classified using their geometrical and topological properties. Finally, the relative position of the classified strokes are examined, and the strokes are combined in several steps into a string of characters that represents the recognized word. Experimental results on texts handwritten by two persons showed high recognition accuracy.