Handwritten numeral recognition based on simplified structural classification and fuzzy memberships

Previous handwritten numeral recognition algorithms applied structural classification to extract geometric primitives that characterize each image, and then utilized artificial intelligence methods, like neural network or fuzzy memberships, to classify the images. We propose a handwritten numeral recognition methodology based on simplified structural classification, by using a much smaller set of primitive types, and fuzzy memberships. More specifically, based on three kinds of feature points, we first extract five kinds of primitive segments for each image. A fuzzy membership function is then used to estimate the likelihood of these primitives being close to the two vertical boundaries of the image. Finally, a tree-like classifier based on the extracted feature points, primitives and fuzzy memberships is applied to classify the numerals. With our system, handwritten numerals in NIST Special Database 19 are recognized with correct rate between 87.33% and 88.72%.     

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.     

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.     

基于统计和结构特征的手写数字识别研究

针对目前手写数字识别精度不高的问题,通过对手写数字图像的研究,提出了基于手写数字图像的空间、旋转、层次和结构特性的特征提取方法.该方法把手写数字的统计和结构特征结合起来,以特征提取方法为基础,利用LibSVM算法对手写数字特征进行了训练和识别.通过实验给出了各个参数的推荐值,利用推荐参数值,手写数字MNIST字体库的识别率高达99.3333％.实验结果表明了该算法在识别手写数字上的有效性和准确性.     

基于主分量分析法的脱机手写数字识别

针对手写数字识别研究中统计特征和结构特征融合困难的问题，利用主分量分析法提取数字字符结构特征的统计信息，重建数字模型，并估计重构偏差，同时提取数字的高宽比特征和欧拉特征，通过组合与3种特征相对应的贝叶斯分类器的分类结果实现数字识别。使用该方法对样本库中的样本进行测试，正确识别率为90.73%。     

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.     

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.     

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。     

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.     

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.     

Lexicon-driven segmentation and recognition of handwritten character strings for Japanese address reading

This paper describes a handwritten character string recognition system for Japanese mail address reading on a very large vocabulary. The address phrases are recognized as a whole because there is no extra space between words. The lexicon contains 111,349 address phrases, which are stored in a trie structure. In recognition, the text line image is matched with the lexicon entries (phrases) to obtain reliable segmentation and retrieve valid address phrases. The paper first introduces some effective techniques for text line image preprocessing and presegmentation. In presegmentation, the text line image is separated into primitive segments by connected component analysis and touching pattern splitting based on contour shape analysis. In lexicon matching, consecutive segments are dynamically combined into candidate character patterns. An accurate character classifier is embedded in lexicon matching to select characters matched with a candidate pattern from a dynamic category set. A beam search strategy is used to control the lexicon matching so as to achieve real-time recognition. In experiments on 3,589 live mail images, the proposed method achieved correct rate of 83.68 percent while the error rate is less than 1 percent.     

结合Fisher判别的手写数字特征提取方法

在手写数字图像的特征提取中,提出一种结合Fisher线性判别的多分辨率Gabor滤波方法,在所有特征点上寻求特定滤波方向上的局部最优滤波频率,以获得最佳滤波效果,同时压缩不相关特征.在MNIST手写数字图像库上的识别实验表明:在小样本情况下,该方法能更准确地抽取手写数字图像特征,识别效果明显优于直接进行Gabor特征提取.     

基于结构特征分类BP网络的手写数字识别

手写体数字识别有着重大的使用价值,用多层BP网络来识别手写体数字是手写体数字识别的一大进步,但是,用单纯的BP网络来识别也存在识别精度不高等的问题。将BP网络技术和数字本身的结构特征结合起来,提出了一种基于结构特征分类BP网络的手写体数字识别新方法。首先提取点、环等数字特征值,并根据一些特征进行分类;然后再运用BP神经网络识别,以提高网络的识别能力;最后,选取了500个人的0~9的手写体数字,运用以上算法进行BP神经网络识别,用3000个手写体数字作为训练样本,2000个其他的样本进行测试,网络收敛后,识别率达到96%以上,比原来有一定的提高。     

基于混合特征的孟加拉手写体数字识别

根据孟加拉手写体数字的特点,用Kirsch算子提取象素的水平、垂直、右对角线以及左对角线的特征矢量,并与字符图像的密度特征相结合,采用BP算法训练的MLP网络作分类器进行识别。最后,用从实际孟加拉信封图像中采集到的手写体数字作样本进行实验,达到了96.1%的识别率。     

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。     

基于模板匹配的手写数字决策分类器设计

针对手写数字识别提出一种基于模板匹配决策分类器设计方法。就该方法下的模式识别分类器设计进行详细论述,给出该分类器算法实现。该算法在对手写的数字图像进行预处理的基础上从待识别的手写数字图像中提取若干特征量与事先建立的标准模板库中模板对应的特征量进行比较,计算待识别图像和标准模板特征量之间的距离,用最小距离法判定其所属类。实验结果表明,该决策分类器算法实现容易,匹配速度快,保证字符识别的正确率。     

小波神经网络在手写数字识别中研究与应用

针对手写数字识别的特点,讨论了数字识别预处理的方法,包括二值化、倾斜矫正、细化和归一化。利用小波函数代替传统神经网络中的激活函数,构建了用于数字识别,小波神经网络系统。仿真结果显示,新系统大大提高了网络训练速度,数字识别的正确率也明显提高。     

A new benchmark on the recognition of handwritten Bangla and Farsi numeral characters

The recognition of Indian and Arabic handwriting is drawing increasing attention in recent years. To test the promise of existing handwritten numeral recognition methods and provide new benchmarks for future research, this paper presents some results of handwritten Bangla and Farsi numeral recognition on binary and gray-scale images. For recognition on gray-scale images, we propose a process with proper image pre-processing and feature extraction. In experiments on three databases, ISI Bangla numerals, CENPARMI Farsi numerals, and IFHCDB Farsi numerals, we have achieved very high accuracies using various recognition methods. The highest test accuracies on the three databases are 99.40%, 99.16%, and 99.73%, respectively. We justified the benefit of recognition on gray-scale images against binary images, compared some implementation choices of gradient direction feature extraction, some advanced normalization and classification methods.     

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。