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。     

Character recognition without segmentation

A segmentation-free approach to OCR is presented as part of a knowledge-based word interpretation model. It is based on the recognition of subgraphs homeomorphic to previously defined prototypes of characters. Gaps are identified as potential parts of characters by implementing a variant of the notion of relative neighborhood used in computational perception. Each subgraph of strokes that matches a previously defined character prototype is recognized anywhere in the word even if it corresponds to a broken character or to a character touching another one. The characters are detected in the order defined by the matching quality. Each subgraph that is recognized is introduced as a node in a directed net that compiles different alternatives of interpretation of the features in the feature graph. A path in the net represents a consistent succession of characters. A final search for the optimal path under certain criteria gives the best interpretation of the word features. Broken characters are recognized by looking for gaps between features that may be interpreted as part of a character. Touching characters are recognized because the matching allows nonmatched adjacent strokes. The recognition results for over 24,000 printed numeral characters belonging to a USPS database and on some hand-printed words confirmed the method's high robustness level     

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.     

一种基于模糊规则的手写体粘连数字串分割

手写数字串切分是手写数字OCR系统中必不可少的组成部分.实际应用中一般用框格对数字的书写范围进行约束,切分过程比较容易,如果没有框格约束,手写数字串的切分就成为一个难题.针对无约束的手写数字串切分的难点,提出了一种新的粘连数字串切分方法.该方法先使用主曲线实现字符模板的笔画抽取,然后依据字符笔画的模糊特征处理笔画,最后以字符识别器提供的置信度为依据完成切分过程.为验证该新切分方法的效果.对从银行实地采集的3 000份真实支票进行了切分实验,其中363张支票存在粘连现象,切分正确率为89.68%.实验结果表明,该算法能够有效地切分多字粘连的手写体数字串.     

On-line recognition of Korean characters using ART neural network and hidden Markov model

This paper proposes an efficient method for on-line recognition of cursive Korean characters. The recognition of cursive strokes and the representation of a large character set are important determinants in the recognition rate of Korean characters. To deal with cursive strokes, we classify them automatically by using an ART-2 neural network. This neural network has the advantage of assembling similar patterns together to form classes in a self-organized manner. To deal with the large character set, we construct a character recognition model by using the hidden Markov model (HMM), which has the advantages of providing an explicit representation of time-varying vector sequence and probabilistic interpretation. Probabilistic parameters of the HMM are initialized using the combination rule for Korean characters and a set of primitive strokes that are classified by the ART stroke classifier, and trained with sample data. This is an efficient means of representing all the 11,172 possible Korean characters. We tested the model on 7500 on-line cursive Korean characters and it proved to perform well in recognition rate and speed.     

工艺美术字计算机变形设计研究

本文提出了一种美术字计算机自动设计的新方法.该方法利用“显示字库”所提供的字形笔划结构特征信息,运用多级数学模型,通过计算机软件功能,对字形自动再加工、再造型、美工优化、艺术化,设计出形式多样的新的艺术字型,是一种新的动态软字库技术.设计字型可应用于电视字幕、广告、印刷排版、电脑动画、大屏幕显示、软件包装等领域。     

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.     

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。     

Off-line handwritten Korean character recognition based on stroke extraction and representation

In this paper, we propose an off-line recognition method for handwritten Korean characters based on stroke extraction and representation. To recognize handwritten Korean characters, it is required to extract strokes and stroke sequence to describe an input of two-dimensional character as one-dimensional representation. We define 28 primitive strokes to represent characters and introduce 300 stroke separation rules to extract proper strokes from Korean characters. To find a stroke sequence, we use stroke code and stroke relationship between consecutive strokes. The input characters are recognized by using character recognition trees. The proposed method has been tested for the most frequently used 1000 characters by 400 different writers and showed recognition rate of 94.3%.     

车辆牌照上英文和数字字符的结构特征分析及提取

为了研制高性能的车辆牌照自动识别系统，在详细分析车辆牌照上英文和数字字符结构特点的基础上，选择字符图象中的闭合曲线作为其整体特征，将笔画端点，三叉点和四叉点作为其细节特征，同时将笔画中的拐角点作为其辅助结构特征，三者可分别用于字符的粗分类，细分类和相似字符区分，进而提基于图论和细节点特征的闭合曲线检测算法以及基于二值图象外边缘轮廓线的笔画拐角点检测算法，将上述结构特征用于车辆牌照上英文和数字字符识别，测得识别率达96％，用PⅢ550计算机完成结构特征抽取和字符识别所用时间约20ms／字符，表明这些结构特征适用于车辆牌照上英文和数字字符的快速识别。     

藏文识别的预处理

预处理是整个文字识别系统的重要组成部分,预处理性能的优劣将直接影响整个识别系统的性能,根据藏文字在字形和书写方式上的特点,实现了一种适用于藏文识别的预处理技术,整个预处理过程包括二值化、版面分析、倾斜校正、字符切和归一化,在预处理过程中还提取了一此圾关字丁的基本特征,这些特征充分反映了藏文的特点,具有良好的稳定性,可以用于识别系统的粗分类和后处理。     

基于图像序列的交互式快速建模系统

给出了一个基于图像序列的交互式三维建模系统.通过输入一段未标定的图像或视频序列,系统能够自动地恢复出摄像机参数;然后用户只需要在少量几帧图像上简单勾画出物体的形态结构,系统就能自动解析出多帧之间用户交互的对应关系,从而迅速、逼真地重建出场景的三维模型.该系统提供了点与线段的重建、直线与平面的重建、曲线与曲面的重建等功能,能够满足对现实世界中的复杂场景的快速高精度的重建要求.几组真实拍摄的图像序列的建模实验表明:该系统高效、实用.能够很好地满足实际建模需求.     

基于笔画曲率特征的笔迹鉴别方法

现有的手写汉字脱机笔迹鉴别方法存在只能针对特定字符或需要大量样本字符等问题,为此提出一种基于笔画曲率特征的笔迹鉴别方法。首先运用数学形态学对采集的笔迹图像进行预处理,在横、竖、撇、捺四个方向提取具有代表性的笔画骨架,然后对笔画骨架进行圆的重构,提取四个方向笔画圆的曲率作为特征值组成笔迹特征矩,根据待鉴别的笔迹特征矩与数据库中笔迹特征矩向量夹角相似性度量结果对样本做出判断。实验结果表明该文方法对于待鉴别样本字符的内容没有要求,样本字符数量要求低、应用范围广、鲁棒性强。     

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。     

Highly stylised animation

In this paper we argue for our NPAR system as an effective 2D alternative to most NPR research, which is focused on frame coherent stylised rendering of 3D models. Our approach gives a highly stylised look to images without the support of 3D models. Nevertheless, they still behave as though they are animated by drawing, which they are. First, a stylised brush tool is used to freely draw extreme poses of characters. Each character is built of 2D drawn brush strokes which are manually grouped into layers. Each layer is assigned its place in a drawing hierarchy called a hierarchical display model (HDM). Next, multiple HDMs are created for the same character, each corresponding to a specific view. A collection of HDMs essentially reintroduces some correspondence information to the 2D drawings needed for inbetweening and, in effect, eliminates the need for a true 3D model. Once the models are composed the animator starts by defining keyframes from extreme poses in time. Next, brush stroke trajectories defined by the keyframe HDMs are inbetweened automatically across intermediate frames. Finally, each HDM of each generated inbetween frame is traversed and all elements are drawn one on another from back to front. Our techniques support highly rendered styles which are particularly difficult to animate by traditional means including the ‘airbrushed’, scraperboard, watercolour, Gouache, ‘ink-wash’, pastel, and the ‘crayon’ styles. In addition, we describe the data path to be followed to create highly stylised animations by incorporating real footage. We believe our system offers a new fresh perspective on computer-aided animation production and associated tools.     

A generic method of cleaning and enhancing handwritten data from business forms

The automation of business form processing is attracting intensive research interests due to its wide application and its reduction of the heavy workload due to manual processing. Preparing clean and clear images for the recognition engines is often taken for granted as a trivial task that requires little attention. In reality, handwritten data usually touch or cross the preprinted form frames and texts, creating tremendous problems for the recognition engines. In this paper, we contribute answers to two questions: “Why do we need cleaning and enhancement procedures in form processing systems?” and “How can we clean and enhance the hand-filled items with easy implementation and high processing speed?” Here, we propose a generic system including only cleaning and enhancing phases. In the cleaning phase, the system registers a template to the input form by aligning corresponding landmarks. A unified morphological scheme is proposed to remove the form frames and restore the broken handwriting from gray or binary images. When the handwriting is found touching or crossing preprinted texts, morphological operations based on statistical features are used to clean it. In applications where a black-and-white scanning mode is adopted, handwriting may contain broken or hollow strokes due to improper thresholding parameters. Therefore, we have designed a module to enhance the image quality based on morphological operations. Subjective and objective evaluations have been studied to show the effectiveness of the proposed procedures. Received January 19, 2000 / Revised March 20, 2001     

基于灰度图像的表格框线去除算法

笔画与表格框线的交叠的现象在表格型文档中普遍存在，严重影响了文档自动处理系统的性能．现有的去线算法大部分都是基于二值图像的，许多有用的局部信息已经丢失．提出了直接利用图像灰度信息的灰值线检测与去除算法．首先利用图像的边缘特征检测直线以及字线的相交位置；然后通过对直线上相交点对的分析确定字线的交叠方式，并将这些方式归纳为穿透和未穿透两类简单的形式；最后将直线划分为保护区和擦除区两部分，保护区内的像素在去线过程中被保留，而擦除区内的像素则利用灰度形态学算法来擦除．在我国现行支票上的实验表明算法是有效的．     

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.