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.     

SEPARATION OF SINGLE-TOUCHING HANDWRITTEN NUMERAL STRINGS BASED ON STRUCTURAL FEATURES

In this paper, we develop a new method to separate single-touching handwritten numeral strings with two numerals using structural features. A binary image of a single-touching handwritten numeral string is preprocessed with an efficient algorithm for smoothing, linearization and detection of structural points of image contours. The touching region of a single-touching handwritten numeral string is determined based on distribution of the structural points in the handwritten numeral string. A candidate touching point is preselected based on the geometrical information of a special structural point in the touching region. In some cases, the left or right lateral numeral of a single-touching handwritten numeral string can be recognized. The recognition information can be utilized to correct the position of the candidate touching point. We have tested our method on image samples taken from the U.S. National Institute of Science and Technology (NIST) database. We used 500 sample images for training and obtained a correct separation rate of 99.1%. For 3287 test samples not used for training the correct separation rate was 97.2%.     

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     

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.     

Segmentation and recognition system for unknown-length handwritten digit strings

The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-length handwritten digit strings by combining several explicit segmentation methods depending on the configuration link between digits. Three segmentation methods are combined based on histogram of the vertical projection, the contour analysis and the sliding window Radon transform. A recognition and verification module based on support vector machine classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Moreover, various submodules are included leading to enhance the robustness of the proposed system. Experimental results conducted on the benchmark dataset show that the proposed system is effective for segmenting handwritten digit strings without prior knowledge of their length comparatively to the state of the art.     

Filtering segmentation cuts for digit string recognition

In this paper we propose a method to evaluate segmentation cuts for handwritten touching digits. The idea of this method is to work as a filter in segmentation-based recognition system. This kind of system usually rely on over-segmentation methods, where several segmentation hypotheses are created for each touching group of digits and then assessed by a general-purpose classifier. The novelty of the proposed methodology lies in the fact that unnecessary segmentation cuts can be identified without any attempt of classification by a general-purpose classifier, reducing the number of paths in a segmentation graph, what can consequently lead to a reduction in computational cost. An cost-based approach using ROC (receiver operating characteristics) was deployed to optimize the filter. Experimental results show that the filter can eliminate up to 83% of the unnecessary segmentation hypothesis and increase the overall performance of the system.     

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.     

基于BP神经网络的手写体数字识别

手写体数字识别是多年来的研究热点,也是字符识别中的一个特别问题。由于手写体数字字体变化很大,传统的识别方法很难达到高的识别率。针对传统的数字识别方法的复杂性和局限性,提出了一种基于BP神经网络的手写体数字的识别方法。该方法在提取手写体数字点特征、笔划密度特征基础上,利用改进的BP神经网络进行训练识别。经实验,识别率达94％。实验结果表明,该方法对手写体数字识别效果良好,不仅简化了传统识别的繁杂性,而且提高了识别的准确性。     

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。     

Combination of the assembly neural network with a perceptron for recognition of handwritten digits arranged in numeral strings

The purpose of the paper is to design and test neural network structures and mechanisms for making use of the information that is contained in the character strings for more correct recognition of the characters constituting these strings. Two neural networks are considered in the paper; both networks are combined into a joint recognition system. The first is the assembly neural network and the second is the neural network of a perceptron type. A computer simulation of the system is performed. The combined system solves the task of recognition of handwritten digits of the MNIST test set provided that the digits have been arranged in the numeral strings memorized in the system. During a recognition process of an input numeral string, the assembly neural network executes intermediate recognition of the digits basing on which a perceptron type network accomplishes the final choice among the limited combinations of strings memorized in the network. The experiments have demonstrated that the combined system is able to make use of the information that is contained in the strings for more correct recognition of digits of the MNIST test set. In particular, the experiments have shown that the combined system commits no errors in the recognition of MNIST test set on the condition that the digits of this set had been organized in the strings of more than 5 digits each.     

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.     

A method for combining complementary techniques for document image segmentation

Image segmentation is a major task of handwritten document image processing. Many of the proposed techniques for image segmentation are complementary in the sense that each of them using a different approach can solve different difficult problems such as overlapping, touching components, influence of author or font style etc. In this paper, a combination method of different segmentation techniques is presented. Our goal is to exploit the segmentation results of complementary techniques and specific features of the initial image so as to generate improved segmentation results. Experimental results on line segmentation methods for handwritten documents demonstrate the effectiveness of the proposed combination method.     

Touching numeral segmentation using water reservoir concept

This paper deals with a new technique for automatic segmentation of unconstrained handwritten connected numerals. To take care of variability involved in the writing style of different individuals a robust scheme is presented here. The scheme is mainly based on features obtained from a concept based on water reservoir. A reservoir is a metaphor to illustrate the region where numerals touch. Reservoir is obtained by considering accumulation of water poured from the top or from the bottom of the numerals. At first, considering reservoir location and size, touching position (top, middle or bottom) is decided. Next, analyzing the reservoir boundary, touching position and topological features of the touching pattern, the best cutting point is determined. Finally, combined with morphological structural features the cutting path for segmentation is generated. The proposed scheme is tested on French bank check data and an accuracy about 94.8% is obtained from the system.     

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.     

Invariant pattern recognition using contourlets and AdaBoost

In this paper, we propose new methods for palmprint classification and handwritten numeral recognition by using the contourlet features. The contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images and handwritten numeral images. AdaBoost is used as a classifier in the experiments. Experimental results show that the contourlet features are very stable features for invariant palmprint classification and handwritten numeral recognition, and better classification rates are reported when compared with other existing classification methods.     

基于识别的手写数字串分割

手写数字串的分割与字符识别密切相关.采用基于识别的分割方法,在分割过程中引入识别机制识别分割碎片,将识别结果经过差值运算后置为每个识别对象的识别可信度,利用动态规划找到最佳分割路径.在训练分类器时,使用反例样本估计分类器参数,得到了性能良好的分类器.实验数据表明,利用正例和反例样本结合训练的分类器比只经过正例样本训练的分类器的识别率要高很多.     

Segmentation of connected handwritten numeral strings

A new approach to separating single touching handwritten digit strings is presented. The image of the connected numerals is normalized, preprocessed and then thinned before feature points are detected. Potential segmentation points are determined based on decision line that is estimated from the deepest/highest valley/hill in the image. The partitioning path is determined precisely and then the numerals are separated before restoration is applied. Experimental results on the NIST Database 19, CEDAR CD-ROM and our own collection of images show that our algorithm can get a successful recognition rate of 96%, which compares favorably with those reported in the literature.     

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。