Adaptive recognition of Chinese characters: imitation ofpsychological process in machine recognition

This paper is focused on imitation of human psychological process in machine recognition of Chinese characters. Some results of research on human Chinese character recognition have been discussed and unified into a compound mechanism with an adaptive and self-developing nature. A machine imitation model has been proposed for Chinese character recognition with different routines. By some simplification but with the crucial feature of the model being retained, an experimental system for handprinted Chinese character recognition based on the novel concept has been built. Experimental results have shown that the associated routines continuously improve their performance during their work even after supervised training is halted. The routine of the global pattern approach eventually learns most of the classes and the recognition process gradually shifts from the subpattern approach to the global pattern approach     

Online recognition of handwritten Hiragana characters based upon acomplex autoregressive model

An online recognition method for handwritten Hiragana characters is developed based upon a complex AR model. The time delay of the AR model is enlarged so that global attributes of handwritten characters are well incorporated into the model, and a character segmentation technique is developed for performance improvement. A good recognition score has been obtained for two different writers     

Handwritten digit recognition: benchmarking of state-of-the-art techniques

This paper presents the results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are CENPARMI, CEDAR, and MNIST. On the test data set of each database, 80 recognition accuracies are given by combining eight classifiers with ten feature vectors. The features include chaincode feature, gradient feature, profile structure feature, and peripheral direction contributivity. The gradient feature is extracted from either binary image or gray-scale image. The classifiers include the k-nearest neighbor classifier, three neural classifiers, a learning vector quantization classifier, a discriminative learning quadratic discriminant function (DLQDF) classifier, and two support vector classifiers (SVCs). All the classifiers and feature vectors give high recognition accuracies. Relatively, the chaincode feature and the gradient feature show advantage over other features, and the profile structure feature shows efficiency as a complementary feature. The SVC with RBF kernel (SVC-rbf) gives the highest accuracy in most cases but is extremely expensive in storage and computation. Among the non-SV classifiers, the polynomial classifier and DLQDF give the highest accuracies. The results of non-SV classifiers are competitive to the best ones previously reported on the same databases.     

基于字型特征的手写体汉字多分类识别的研究

针对汉字特点,提出一种基于汉字结构度和繁简度二类模态判别的多模式识别法。给出了汉字字型结构度类型的字型编码,以及汉字字型结构分解算法;对分解后的部件进行繁简度判断,依据各部件繁简度模态选择合适的特征提取算法,实现手写体汉字字型分解的多模式识别方法融合;对相似字采用两级分类的识别法,从而提高汉字的识别率和正确率。仿真实验验证了方法的有效性。     

基于特征反馈的手写体汉字识别系统研究

针对传统基于开环的汉字识别系统不完全符合人类识字过程的问题,构建了一种具有反馈结构的仿人智能识别系统。该系统根据待识别汉字的多模态定性识别结果来选择最佳的首轮识别方案,在完成识别之后,提取广义识字误差对候选字进行可信度判断和反馈校正。设计了3种广义识字误差,通过对这3种广义识字误差的类型和数值进行定性与定量相结合的分析,建立了识别结果的可信度评价指标体系和反馈校正决策机制。仿真实验结果验证了方法的可行性。     

Machine recognition of printed Chinese characters via transformation algorithms

This paper presents some novel results concerning the recognition of single-font printed Chinese characters via the transformation algorithms of Fourier, Hadamard, and Rapid. The new design philosophy of a three-stage structure is believed to offer at least a suboptimal search strategy for recognizing printed Chinese characters with a dictionary of 7000–8000 characters. The transformation algorithms discussed in this paper will be used in the last two stages. Extensive experiments and simulations concerning feature extraction and noisy or abnormal pattern recognition have been carried out (the simulations have been restricted to a 63-character subset called “Radicals”). Comparison has been made of all three transforms according to their ability to recognize characters.     

2-D EAG Method for the Recognition of Hand-Printed Chinese Characters

A method,called Two-Dimensional Extended Attribute Grammars(2-D EAGs) for the recognition of hand-printed Chinese characters is presented.This method uses directly two dimensional information,and provides a scheme for dealing with various kinds of specific cases in a uniform way.In this method,components are drawn in guided and redundant way and reductions are made level by leve just in accordance with the component combination relations of Chinese characters.The method provids also polysemous grammars,coexisting grammars and structure inferrings whih constrain redundant recognition by comparison among similar characters of components and greatly increase the tolerance ability to distortion.     

Preprocessing for Chinese character recognition and global classification of handwritten Chinese characters

Techniques for calculating the stroke directions of thinned binary characters and for detecting the intersections and end points of strokes by means of pattern matching and weighting method are proposed as a preprocessing of handwritten Chinese character recognition. We also propose a method for global classification of handwritten Chinese characters by means of projection profiles of strokes and show that the method is available for the Chinese characters written in the square style.     

基于DPM的自然场景下汉字识别方法

自然场景下, 汉字背景复杂且形态各异, 导致传统识别方法中的文本定位与文本矫正过程难以进行。为了避免这些问题, 采用物体识别方法中的可变部件模型（DPM）进行识别。该方法将汉字视为物体类, 训练其对应的参数模板, 然后采用滑动窗口的方法遍历待检测图片, 以判断图片中是否存在目标汉字。实验表明, 该方法对简单独体汉字有较好的检测效果, 但对于多笔画复杂汉字, 由于模型自身结构特点, 效果并不明显。     

利用双向SOFM网络模拟汉字认知过程的研究

在汉字的认知过程中有“字优效应”和“字劣效应”,前者认为在汉字认知过程中整字信息优于部件或笔画信息,后者反之。以自组织特征映射算法为理论基础,提出了一种双向自组织特征映射(SOFM)网络,利用自组织网络实现根据汉字和部件多维表征的聚类,并建立两层网络之间的连接关系,通过双向测试,得到不同构型汉字所具有的字优效应和字劣效应,从新的角度实现了SOFM的应用。研究结果对于汉字教学方法有一定的参考价值。     

基于ICA降维的车牌汉字识别研究

针对目前复杂环境下车牌汉字图像识别率较低,识别时间较长等问题,提出了一种基于伪Zernike矩和独立主成分分析(ICA)的改进概率神经网络(PNN)车牌汉字识别方法.该方法是将车牌汉字图像的伪Zernike矩通过独立主成分分析降维,再将降维后的特征输入所提出的一种基于代表点的改进概率神经网络中进行训练和识别,从而有效地实现车牌汉字的识别.将该方法应用于复杂环境下的车牌汉字图像识别实验,实验结果表明,该方法能有效地降低特征维数,减少识别时间,并能显著地提高车牌汉字的识别率.     

小波包变换在手写体金融汉字识别中的应用

针对小波包变换的特点,提出了一种基于小波包变换的手写体金融汉字识别算法。该算法首先对汉字图像进行二维小波包分解,利用基于子图像能量方差的准则选择适当的部分分解树;然后将得到的子图像划分成多个局部窗口,计算局部窗口的能量值组成特征向量;再通过主成分分析(PCA)选择分类能力最强的一组特征,降低特征空间的维数;最后,将特征向量送入支持向量机进行分类。实验结果表明,该算法取得了较好的识别效果。     

基于多分类器集成的手写体金融汉字识别

用基于遗传算法的全局优化技术动态地选择一组分类器,并根据应用的背景,采用合适的集成规则进行集成,从而综合了不同分类器的优势和互补性,提高了分类性能。实验结果表明,通过将遗传算法引入到多分类器集成系统的设计过程,其分类性能明显优于传统的单分类器的分类方法。     

Online handwriting recognition: the NPen++ recognizer

This paper presents the online handwriting recognition system NPen++ developed at the University of Karlsruhe and Carnegie Mellon University. The NPen++ recognition engine is based on a multi-state time delay neural network and yields recognition rates from 96% for a 5,000 word dictionary to 93.4% on a 20,000 word dictionary and 91.2% for a 50,000 word dictionary. The proposed tree search and pruning technique reduces the search space considerably without losing too much recognition performance compared to an exhaustive search. This enables the NPen++ recognizer to be run in real-time with large dictionaries. Initial recognition rates for whole sentences are promising and show that the MS-TDNN architecture is suited to recognizing handwritten data ranging from single characters to whole sentences. Received September 3, 2000 / Revised October 9, 2000     

基于FSVM脱机手写体汉字分类识别研究

针对脱机手写体汉字特点,给出一种采用模糊支持向量机粗分类的方法。根据小波分解像素密度特征,利用模糊支持向量机对汉字进行粗分类。细分类识别提取外围特征,同时融合小波多网格特征,采用一对多算法进行细识别。仿真实验表明,该方法有较高识别率。     

基于模板匹配的移动设备手写汉字笔画识别

针对当前移动设备上手写汉字流行的[xml]文件存储格式,提出了一种对用户字笔画与模板字笔画测试匹配的算法,该算法通过方位、拓扑关系和形状3种特征综合量度笔画间的匹配,实验效果良好。该算法在用户字的多笔少笔判别、笔顺的正误性判别、整字的正确性以及工整性判别等方面都有着广泛的应用。     

On-line recognition of handprinted Kanji characters

Difficulties in Kanji (Chinese character) recognition stem from its large character set (about 5000 characters) and the large number of strokes (up to about sixty) in each character.

The paper describes a preliminary approach to this Kanji recognition problem. In the present method, a handprinted Kanji character is coded into a symbol string using the binary relation between stroke and reference zone. Two symbol string recognition methods are proposed and investigated; the direct matching recognition (DMR) method and the unit structure recognition (USR) method.

The DMR method worked efficiently for characters which have up to five strokes. The USR method represents Kanji characters with a structural unit combination. This method worked efficiently for multi-stroke characters and greatly reduced dictionary update labor.     

Computer recognition of printed Tamil characters

Computer recognition of machine-printed letters of the Tamil alphabet is described. Each character is represented as a binary matrix and encoded into a string using two different methods. The encoded strings form a dictionary. A given text is presented symbol by symbol and information from each symbol is extracted in the form of a string and compared with the strings in the dictionary. When there is agreement the letters are recognized and printed out in Roman letters following a special method of transliteration. The lengthening of vowels and hardening of consonants are indicated by numerals printed above each letter.     

On-line recognition of hand-printed Korean characters

The Korean alphabet is a set of phonetic symbols which are combined to form characters, somewhat in the Chinese style. The phonetic quality of the symbols naturally limits the combinations that are useful, and character formation constraints further limit these combinations. Advantage of this is made in an on-line computer recognition system. Korean characters are entered by means of a graphic tablet, using the standard stroke sequences taught in schools. Recognition is perfect for carefully drawn characters and nearly so for characters written at an unhurried rate, provided the system is tuned to the writer's style.