手绘太阳黑子图手写字符分割方法研究

利用固定区域坐标提取固定区域的单行数据字符块;基于高斯模糊提取随机手写黑子信息字符区;结合Hough变换与投影技术完成随机手写区域中包含多个黑子记录字符块的分割,并将包含单个黑子记录字符块分割为3个仅包含单行数据的字符块;利用颜色填充分割算法分割出单行数据字符块中的单个字符和粘连字符,结合平均字符宽度信息进一步分割粘连字符。实验结果表明,每幅手绘太阳黑子图的固定区域和随机区域的字符分割平均正确率分别达到95.5%和79.6%。     

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。     

一种机读旅行证件信息自动识别方法

分析了旅行证件信息自动识别的特点,提出了一种针对机读旅行证件图像特点的信息自动识别方法,包括基于连通区域分析的机读码区域定位、基于线性拟合的机读码区域图像倾斜校正、基于先验知识约束的自适应字符图像分割以及基于字符结构特征分析的字符识别等四个主要步骤。并在此基础上实现了签证信息自动识别系统。实验表明该方法对旅行证件信息进行自动提取和识别具有较好的效果。     

基于改进FCM聚类算法的维吾尔文字图像分割

研究维吾尔文字图像分割问题,针对传统的FCM聚类算法对维吾尔文字符图像分割时相邻域的信息未能考虑,故容易造成其对维吾尔文字符图像分割时的缺陷和干扰问题,为解决上述问题,提出了一种改进型FCM聚类算法的维吾尔文字符图像分割的方法。首先通过多尺度图像锥建立了其聚类算法,减少维吾尔文字符图像分割时的数据大小,进而一步降低了维吾尔文字符图像分割本身的计算量,然后通过空间信息的引入,使干扰噪声信息被屏蔽,从而提高维吾尔文字图像聚类分割的抗干扰能力。仿真结果表明,算法更容易提高维吾尔文字图像分割效果,准确分割出维吾尔文字区域,提高了识别精度,优于一般的FCM聚类算法。     

多信息融合的快速车牌定位

目的车牌定位是车牌识别的关键步骤之一,为提高车牌定位的准确率和定位速度,降低误检率,提出一种基于多信息融合的快速车牌定位方法。方法首先,通过边缘密度信息快速排除大量背景区域,有效提高定位速度;其次,根据车牌字符的分布信息精确定位车牌;最后使用基于模板匹配的车牌字符分割方法进行车牌字符分割,通过验证所分割出的字符是否是数字或字母来验证所定位区域是否车牌,去除误检车牌区域。结果在9 980幅图像上进行测试,定位准确率为97.9%,平均定位时间为16.3ms。实验结果表明,本文方法可以在各种条件下快速而准确地定位车牌。结论融合多种特征的车牌定位方法,通过提取车牌对应的环境信息,排除了大量的背景区域,加快了车牌定位的速度;根据车牌的结构信息定位车牌,并通过车牌的部件信息,实现合法性验证,提高车牌定位的准确率。通过多种信息的融合,有效改善了车牌定位的效果。     

构造化线段特征矩阵的手写汉字识别

本文介绍了一种利用线段特征矩阵进行匹配的手写汉字识别方法。对输入文字图像测定其笔划宽度，抽取四个方向子图像。然后，利用文字图像重心分割图像成若干区域，按分割的区域，求各子图像区域的线段特征矩阵，与样本字库比较识别，进行手写汉字识别分类。实验表明本方法是有效的。     

一种新颖的模糊字符图像分割自适应形态学方法

本文提出了一种新颖的字符分割以及从主要的模糊图像中提取特征矢量的自适应方法。算法基于分割分段字符之前自动检测的碎片和合并这些碎片的直方图描述。对于重叠字符的分割，形态学分级算法自动确定参考线；对于连接字符的分割，形态学细化算法和分割费用计算自动检测基准线。我们的方法可以检测碎片、重叠或连接的字符及其自适应应用。更为重要的是，在实验中，模糊图像作为容许标识的图像，以减少有效区域的使用来评估其坚固性、柔韧性以及方法的有效性。系统方式的数据输出．如特征矢量保留了较多正确的有用信息，在自动图像识别系统中作为输入数据。     

基于Mean-Shift的图像文本信息提取

图像和视频中包含着丰富的文本信息,提取和识别图像文本信息非常具有实际意义。传统的图像文本信息提取方法大多基于字符的代数和几何特征。作者从另一个角度出发,将彩色字符看成彩色图像的一部分,使类似字符的景物也可以被当作字符识别出来。提出一种基于Mean-Shift算法的图像文本信息提取方法,首先利用Mean-Shift算法对图像进行分割,然后对分割得到的文本区域进行投影分析从而将每个字符分割出来,最后将字符识别。     

基于Mean-Shift的图像文本信息提取

图像和视频中包含着丰富的文本信息,提取和识另4图像文本信息非常具有实际意义。传统的图像文本信息提取方法大多基于字符的代数和几何特征。作者从另一个角度出发,将彩色字符看成彩色图像的一部分,使类似字符的景物也可以被当作字符识别出来。文中提出一种基于Mean-Shift算法的图像文本信息提取方法,首先利用Mean-Shift算法对图像进行分割,然后对分割得到的文本区域进行投影分析从而将每个字符分割出来,最后将字符识别。     

一种基于边缘信息的车牌图像分割方法

为提高车牌预处理过程中字符与背景的分隔效果,提高识别准确率,从人类视觉分析的特点出发,首先根据车牌图像中汉字字符和字母数字不同的统计特性对最优的阶梯型边缘检测算法进行了改进;并提出了一种新的动态分割,区域处理的方法。实验表明本方法能够有效提取车牌字符特别是汉字中的较复杂的细节信息,保证了字符边缘的准确性以及内部联通区域的一致性,并对不同质量的车牌图像具有一定的普适性,为识别提供了良好的保证。     

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.     

A graph-based approach for segmenting touching lines in historical handwritten documents

Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data.     

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。     

最小加权分割路径的古籍手写汉字多步切分方法

针对古籍古文献中部分汉字易发生粘连现象,提出一种古籍手写汉字多步分割方法.该方法继承了以往粗分割和细分割相结合的思想,首先采用投影进行粗分割,将手写汉字分为粘连字符和非粘连字符两类;然后针对粘连字符串抛弃常用的串行模式,直接采用粗分割的统计信息,设置初始分割路径,并基于最短分割路径的思想,在初始分割路径的局部邻域内基于最小权值搜索并修改分割路径,从而获得最佳的加权分割路径.实验证明该方法解决了字符分割不足和多处粘连字符的分割问题,有效的提高了分割的准确率,且算法的时间复杂度较低,算法效率较高.     

A new methodology for gray-scale character segmentation andrecognition

Generally speaking, through the binarization of gray-scale images, useful information for the segmentation of touched or overlapped characters may be lost in many cases. If we analyze gray-scale images, however, specific topographic features and the variation of intensities can be observed in the character boundaries. In this paper, we propose a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images. In the proposed methodology, the character segmentation regions are determined by using projection profiles and topographic features extracted from the gray-scale images. Then a nonlinear character segmentation path in each character segmentation region is found by using multi-stage graph search algorithm. Finally, in order to confirm the nonlinear character segmentation paths and recognition results, a recognition-based segmentation method is adopted. Through the experiments with various kinds of printed documents, it is convinced that the proposed methodology is very effective for the segmentation and recognition of touched and overlapped characters     

Segment confidence-based binary segmentation (SCBS) for cursive handwritten words

A novel segment confidence-based binary segmentation (SCBS) for cursive handwritten words is presented in this paper. SCBS is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, SCBS is an unordered segmentation approach. SCBS is repetition of binary segmentation and fusion of segment confidence. Each repetition generates only one final segmentation point. The binary segmentation module is a contour tracing algorithm to find a segmentation path to divide a segment into two segments. A set of segments before binary segmentation is called pre-segments, and a set of segments after binary segmentation is called post-segments. SCBS uses over-segmentation technique to generate suspicious segmentation points on pre-segments. On each suspicious segmentation point, binary segmentation is performed and the highest fusion value is recorded. If the highest fusion value is greater than the one of pre-segments, the suspicious segmentation point becomes the final segmentation point for the iteration. If not, no more segmentation is required. Segment confidence is obtained by fusing mean character, lexical and shape confidences. The proposed approach has been evaluated on local and benchmark (CEDAR) databases.     

一种有效的手写汉字多步分割方法

在字符识别系统中,字符的有效分割是识别的关键。针对手写汉字字间距及字内距无规则可循,字符间极易发生粘连、交错等现象,提出一种多步分割方法。该方法首先利用Viterbi算法将原字符串切分成互不连通的分割块,使非粘连汉字、交错汉字得到正确分割;对于其中宽度较大存在粘连字符的分割块,从候选分割点入手,用非线性分割路径将粘连部分分开;最后再应用A*算法找到全局最佳分割位置,使过分割的字符得到完整合并。实验结果表明,该方法对于手写汉字的分割是可行、有效的。     

Recovery of drawing order from multi-stroke English handwritten images based on graph models and ambiguous zone analysis

Recovery of the drawing order of strokes in a handwritten image can be seen as searching for the smoothest path for each stroke on an undirected graph that is constructed from the skeleton of the handwritten image. However, this requires correcting for separating strokes, and detecting starting points. Moreover, ambiguousness at junction points increases the complexity of finding the smoothest paths. In order to resolve these issues, an effective approach that can simultaneously detect the points to separate strokes and find the optimal path for each stroke is proposed. To reduce the complexity of the problem, the skeleton graph of the handwritten image is used, and touching characters or crossing strokes are separated. Touches or crossings of stroke parts at ambiguous zones are detected and the smoothness values are adjusted to improve the accuracy. The greedy algorithm and Dijkstra'salgorithm with a well-defined function of smoothness are applied in searching the optimal path. The nature of the recovery is increased when the optimal path is split into many strokes by using the curvatures of the edges, the un-smoothness between edges and the appearance of double-traced edges. Finally, pixel sequences of strokes are extracted and ordered by using rules of handwriting. The effectiveness of the proposed method is demonstrated through low error rates of pixel sequence comparison and high accuracy of online recognition.     

车牌字符识别的预处理算法

文章介绍了车牌图像的二值化和字符分割算法。提出了一种图像二值化的改进算法,应用简单统计法及Roberts边缘检测算子,在具有噪声以及灰度不均匀的复杂背景中提取出待识别的字符,同时运用数学形态学进行二值图去噪。最后讨论了基于投影法的车牌字符分割方法,取得了较满意的结果。     

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.