An over-segmentation method for single-touching Chinese handwriting with learning-based filtering

The segmentation of touching characters is still a challenging task, posing a bottleneck for offline Chinese handwriting recognition. In this paper, we propose an effective over-segmentation method with learning-based filtering using geometric features for single-touching Chinese handwriting. First, we detect candidate cuts by skeleton and contour analysis to guarantee a high recall rate of character separation. A filter is designed by supervised learning and used to prune implausible cuts to improve the precision. Since the segmentation rules and features are independent of the string length, the proposed method can deal with touching strings with more than two characters. The proposed method is evaluated on both the character segmentation task and the text line recognition task. The results on two large databases demonstrate the superiority of the proposed method in dealing with single-touching Chinese handwriting.     

Segmentation of connected handwritten digits using Self-Organizing Maps

Segmentation is an important issue in document image processing systems as it can break a sequence of characters into its components. Its application over digits is common in bank checks, mail and historical document processing, among others. This paper presents an algorithm for segmentation of connected handwritten digits based on the selection of feature points, through a skeletonization process, and the clustering of the touching region via Self-Organizing Maps. The segmentation points are then found, leading to the final segmentation. The method can deal with several types of connection between the digits, having also the ability to map multiple touching. The proposed algorithm achieved encouraging results, both relating to other state-of-the-art algorithms and to possible improvements.     

The recognition of handwritten numeral strings using a two-stage HMM-based method

In this paper, a two-stage HMM-based recognition method allows us to compensate for the possible loss in terms of recognition performance caused by the necessary trade-off between segmentation and recognition in an implicit segmentation-based strategy. The first stage consists of an implicit segmentation process that takes into account some contextual information to provide multiple segmentation-recognition hypotheses for a given preprocessed string. These hypotheses are verified and re-ranked in a second stage by using an isolated digit classifier. This method enables the use of two sets of features and numeral models: one taking into account both the segmentation and recognition aspects in an implicit segmentation-based strategy, and the other considering just the recognition aspects of isolated digits. These two stages have been shown to be complementary, in the sense that the verification stage compensates for the loss in terms of recognition performance brought about by the necessary tradeoff between segmentation and recognition carried out in the first stage. The experiments on 12,802 handwritten numeral strings of different lengths have shown that the use of a two-stage recognition strategy is a promising idea. The verification stage brought about an average improvement of 9.9% on the string recognition rates. On touching digit pairs, the method achieved a recognition rate of 89.6%. Received June 28, 2002 / Revised July 03, 2002     

Multi-oriented touching text character segmentation in graphical documents using dynamic programming

The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.     

Reading handwritten digits: a ZIP code recognition system

A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail     

A selection framework of sensor combination feature subset for human motion phase segmentation

Motion phase plays an important role in the spatial–temporal parameters of human motion analysis. Multi-sensor fusion technology based on inertial sensors frees the monitoring of the human body phase from space constraints and improves the flexibility of the system. However, human phase segmentation methods usually rely on the determination of the positioning of the sensor and the number of sensors, it is difficult to artificially select the number and position of the sensors, especially when human motion phases are diverse. This paper proposes a selection framework for the sensor combination feature subset for motion phase segmentation, which combines feature selection algorithms with the subsequent classifiers, and determine the optimum combination of the sensor and the feature subset according to the performance of the trained model. Through the constraint and the sensor combination feature subset (SCFS), the filter method can select any number of sensors and control the size of the feature subset; the embedded method can select any number of sensors, but the size of the feature subset is determined by the classifier model. Experimental results show that the proposed framework can effectively select a specified number of sensors without human intervention, and the number of sensors has an impact on the recognition rate of the classifier within 1.5%. In addition, the filter method has good adaptability to a variety of classifiers, and the classifier prediction time can be controlled by setting the subset size of the feature; the embedded method can achieve a better phase segmentation effect than the filter method. For the application of motion phase segmentation, the proposed framework can reliably and quickly identify redundant sensors that provide effective support for reducing the complexity of the wearable sensor system and improving user comfort.     

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.     

一种数字仪表显示值识别的预处理算法*

首先使用一种综合多帧差异积累、形态滤波和Sobel边缘检测的方法实现了数字区域的准确定位分割,然后采用基于局部门限处理的方法,解决了图像亮度分布不均的二值化问题,最后运用连续子集迭代分割算法实现了粘连和断裂数字的准确切分。     

基于两段多组件图割的非监督彩色图像分割算法

提出基于两段多组件图割的彩色图像分割算法,以解决因标签过多和噪声导致的过分割和图割算法低效等问题.多组件图割算法分割图像时,把标签相同的区域处理为该标签的多个组件,结合两层高斯金字塔形成两段多组件图割,以减少分割错误和标签数量,提高分割的性能.算法首先提取基于多尺度四元数Gabor滤波的texton纹理特征,并自适应融合颜色特征;然后使用两段多组件图割获取图像的优化分割,其中,为了引导图割优化的方向,在平滑项中引入彩色梯度信息;最后去除分割结果中的弱边界,获得最终的分割结果.实验结果表明,相对于比较算法,新算法的分割性能有明显提升.     

Color image segmentation using pixel wise support vector machine classification

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.     

条件颗粒分割方法研究

图像中两个物体的接触关系根据粒径可分为未接触、不同粒度物体的接触和同粒度物体的接触3种，接触物体根据接触部分的大小又可分为强接触、中等接触和弱接触。对接触物体的保形分割应该是使分割后物体恢复原来的形态，鉴于流域分割、测地重建等算法在分割接触物体时，不但对物体形态产生破坏和受干扰因素多，而且对于计算接触面积大小的问题，以上算法也不易实现，为此，提出了条件颗粒分割方法，即在数学形态学开运算过程中，对标定区域腐蚀后，不再做膨胀运算，就直接在保形的基础上，对不同粒度的物体进行分割，而对于同粒度接触的物体，则先通过腐蚀后，再做一次特殊条件颗粒分割来得到小粒度(条带)部分，再进行复原就是目标物体的接触部位。最后，介绍了此算法在岩石颗粒粒度分析及胶结类型划分方面的应用。     

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.     

基于数学形态学的灰度图像连接物体分割方法

由于噪声的存在以及连接物体的特点,传统的标记分水岭算法对包含连接物体的灰度图像很难取得满意的分割结果;特别是在背景并不连通的情况下,误分割更为常见;在标记分水岭算法的基础上,提出了一种连接物体分割方法;将属于鲁棒统计的Hough变换用于提取物体标记扩展了标记分水岭算法的应用范围;针对在分割连接物体时,由于背景并非连通,因此允许背景被分别标记,并通过一个后续滤波步骤用以剔除分割后图像中的背景部分,从而得到精确的分割图像;试验证明该算法运算速度快,鲁棒性好,具有广泛的应用价值.     

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.     

Color image segmentation using automatic pixel classification with support vector machine

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.     

Recognition of Handwritten ZIP Codes in a Real—World Non-Standard-Letter Sorting System

In this article, we describe the OCR and image processing algorithms used to read destination addresses from non-standard letters (flats) by Siemens postal automation system currently in use by the Deutsche Post AG¹.We first describe the sorting machine, its OCR hardware and the sequence of image processing and pattern recognition algorithms needed to solve the difficult task of reading mail addresses, especially handwritten ones. The article concentrates mainly on the two classifiers used to recognize handprinted digits. One of them is a complex time delayed neural network (TDNN) used to classify scaled digit-features. The other classifier extracts the structure of each digit and matches it to a number of prototypes. Different digits represented by the same graph are then discriminated by classifiying some of the features of the digit-graph with small neural networks.We also describe some approaches for the segmentation of the digits in the ZIP code, so that the resulting parts can be processed and evaluated by the classifiers.     

模糊C 均值聚类与多相水平集图割优化相结合的图像分割

针对在分割多个目标时多相水平集模型对初始轮廓曲线敏感且计算量大的问题, 提出采用模糊C 均值聚类算法将图像进行粗分割,初始化多相水平集函数,使用图割算法分割 出多相结果的方法。该方法能有效减小多相水平集算法对初始轮廓曲线的敏感性,使图割算法 在分割图像时更容易分割出理想的目标轮廓;同时,采用图割算法可使水平集函数很快收敛到 能量最小值,有效减少计算量,提高计算效率。实验表明该方法具有较好地分割效果和较高地 分割效率。     

基于对抗网络的验证码识别方法

在人机智能交互中,让机器自动识别验证码是机器模拟人的一项基础技术。基于文本的验证码识别一般先对验证码图片进行预处理,然后切割,最后对字符分类识别。字符切割的准确程度直接影响最终识别结果。提出一种对抗学习方法识别文本型验证码。先训练一个Pix2pix网络对验证码图片进行预处理,然后对抗训练出一对分割和识别网络。分割网络不仅能分割粘贴字符,而且可以筛选出难以分割的验证码结果。识别网络采用上下文相关的多通道卷积网络,能有效解决分割过程中因信息丢失而无法识别的问题。实验结果表明,该方法能提高文本验证码识别的准确率。     

融合显著性分析与图割的姿态无关服装区域分割算法

提出了一种针对多姿态人的服装区域分割算法,通过融合显著性分析和图割方法有效地提高了服装区域分割的性能.首先,提出一种基于滑动窗口的视觉显著性区域分析方法,计算前景?背景种子区域初始定位,实现种子区域定位的姿态无关性;然后,通过基于图的分割方法对初始种子区域进行矫正;最后,通过将种子区域作为输入的迭代图割方法——GrabCut获得服装区域分割.实验结果表明,文中算法具有较好的分割性能,具有应用前景.