Handwritten Chinese character recognition by rule-embedded Neocognitron

This paper presents an attempt to integrate neural computation with a domain knowledge technique to resolve the problem of the wide variety in handwritten Chinese characters. Despite their complexity, Chinese characters can be seen as structured patterns. Therefore, we propose a symbolic representation to describe these structural formations. In particular, we consider the Fuzzy Attributed Production Rule (FAPR) as a possible symbolic representation. On the neural computational side, we study Fukushima's Neocognitron model, which has been successfully demonstrated to recognize handwritten alphanumerics. Despite its power and tolerance capabilities, the supervised training scheme used by Fukushima is impractical for a large character set such as Chinese characters. We thus propose a ruleembedded Neocognitron network which can be readily mapped with structure-knowledge of Chinese characters as represented in FAPRs. In this paper, we demonstrate how 50 Chinese characters are mapped onto the network, and that the system performance in tolerating character structure deviations is satisfactory.     

基于模糊子笔画统计特征的手写体汉字识别

汉字由笔画或子笔画组成,笔画或子笔画特征在手写体汉字识别中得到了广泛应用。论文提出一种模糊子笔画抽取方法,解决了因无限制手写体笔画随意性而使得抽取的子笔画不稳定的问题。计算字符边缘点“横”、“竖”、“撇”、“捺”的模糊子笔画属性特征,并将其与模糊网格相结合,生成模糊子笔画统计特征。银行支票手写体汉字大写金额识别的实验结果表明应用模糊子笔画统计特征能取得更好的识别效果。     

基于双弹性网格的手写体汉字识别

特征提取是手写体汉字识别的关键,目前四方向网格特征已被实验证实是一种较好的手写体汉字特征。针对通常的纵横弹性网格对汉字“撇、捺”笔画特征提取的不足,提出一种新的网格构造技术——对角弹性网格,它由45°和135°的对角直线构成,将汉字图像划分为多个菱形,能够很好地适应汉字在“撇、捺”方向的变化。将这两种网格单独,以及相互组合成双网格等情况分别进行手写体识别实验,实验结果验证了对角弹性网格的有效性和双弹性网格的高识别率性。     

Handwritten Chinese character recognition using nonlinear active shape models and the Viterbi algorithm

Since Chinese characters are composed from a small set of fundamental shapes (radicals) the problem of recognising large numbers of characters can be converted to that of extracting a small number of radicals and then finding their optimal combination. In this paper, radical extraction is carried out by nonlinear active shape models, in which kernel principal component analysis is employed to capture the nonlinear variation. Treating Chinese character composition as a discrete Markov process, we also propose an approach to recognition with the Viterbi algorithm. Our initial experiments are conducted on off-line recognition of 430,800 loosely-constrained characters, comprised of 200 radical categories covering 2154 character categories from 200 writers. The correct recognition rate is 93.5% characters correct (writer-independent). Consideration of published figures for existing radical approaches suggests that our method achieves superior performance.     

卷积深度神经网络的手写汉字识别系统

针对传统两级手写汉字识别系统中手写汉字识别的特征提取方法的限制问题,提出了一种采用卷积神经网对相似汉字自动学习有效特征进行识别的系统方法。该方法采用来自手写云平台上的大数据来训练模型,基于频度统计生成相似子集,进一步提高识别率。实验表明,相对于传统的基于梯度特征的支持向量机和最近邻分类器方法,该方法的识别率有一定的提高。     

Handwritten alphanumeric character recognition by the neocognitron

A pattern recognition system which works with the mechanism of the neocognitron, a neural network model for deformation-invariant visual pattern recognition, is discussed. The neocognition was developed by Fukushima (1980). The system has been trained to recognize 35 handwritten alphanumeric characters. The ability to recognize deformed characters correctly depends strongly on the choice of the training pattern set. Some techniques for selecting training patterns useful for deformation-invariant recognition of a large number of characters are suggested.     

Handwritten character recognition through two-stage foreground sub-sampling

In this paper, we present a methodology for off-line handwritten character recognition. The proposed methodology relies on a new feature extraction technique based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible. Feature extraction is followed by a two-stage classification scheme based on the level of granularity of the feature extraction method. Classes with high values in the confusion matrix are merged at a certain level and for each group of merged classes, granularity features from the level that best distinguishes them are employed. Two handwritten character databases (CEDAR and CIL) as well as two handwritten digit databases (MNIST and CEDAR) were used in order to demonstrate the effectiveness of the proposed technique. The recognition result achieved, in comparison to the ones reported in the literature, is the highest for the well-known CEDAR Character Database (94.73%) and among the best for the MNIST Database (99.03%)     

基于压缩传感的手写字符识别方法

基于新出现的压缩传感理论,提出了一种鲁棒的手写字符识别方法,能很好地对含有噪声的字符进行识别.该方法通过对测试字符进行稀疏表示,采用l1范数最小化算法求得最稀疏的系数解,所获得的系数具有明显的类别信息,从而易于对测试字符进行分类.实验结果表明,该方法具有很好的噪声鲁棒性.     

打印文件鉴别打印机型的文字图像模糊识别*

探索了一种以打印件鉴别打印机型的文字图像计算机模糊识别方法.该方法收集标准常用字号和字体,以及常用打印机打印的文字,扫描采集,用改进的直方图波形分析法处理图像,提取文字的笔画总面积和笔画轮廓总周长等特征指标;再选定一种机型为参照,对各种机型相同字上述指标测量值及其几种组合的计算值,形成相对差值指标序列,建立信息数据库.在此基础上,建立对应指标的统计均值波动区间的值域表,并确定各指标的权重和建立权重系数矩阵.判断未知机型时,先按照前述方法任测100个常用字,利用OCR汉字识别模块和前述指标,自动辨识文字,进入模糊识别过程.根据相应检测字在值域表区间出现的概率,建立模糊关系矩阵.通过两个矩阵乘积的模糊变换产生判别矩阵.以最大隶属性确定打印机类型.按照数学模型,设计并实现打印机智能鉴别程序.应用实例测试,结果显示判别准确,符合设计预期.     

基于多级分类器和神经网络集成的手写体汉字识别

为了提高系统的泛化能力,在分析了当前汉字识别最新发展技术的基础上,提出了一种三级识别策略的汉字识别系统模型.第一级,使用传统的外围特征法将待选字进行粗分;第二级,使用笔划密度特征法进行细分;第三级,使用一种基于球领域模型的神经网络集成算法对结果进行最后的确认.模拟算法证明,它可以更进一步地提高系统的泛化能力.     

Handwritten word recognition with character and inter-characterneural networks

An off-line handwritten word recognition system is described. Images of handwritten words are matched to lexicons of candidate strings. A word image is segmented into primitives. The best match between sequences of unions of primitives and a lexicon string is found using dynamic programming. Neural networks assign match scores between characters and segments. Two particularly unique features are that neural networks assign confidence that pairs of segments are compatible with character confidence assignments and that this confidence is integrated into the dynamic programming. Experimental results are provided on data from the U.S. Postal Service.     

手写汉字的集群识别

为了降低单个汉字的分辨率,论文分析了通用的汉字识别模型,并在此基础上建立了适于多字识别的集群识别模型。为了充分论证集群识别模型的观点,本文从理论证明和实验两方面获得支持根据。实验结果表明基于多字识别模型的集群识别能可靠提高对连续文字的识别效果,是手写汉字识别中很有希望的发展方向。     

Handwritten Chinese radical recognition using nonlinear active shape models

Handwritten Chinese characters can be recognized by first extracting the basic shapes (radicals) of which they are composed. Radicals are described by nonlinear active shape models and optimal parameters found using the chamfer distance transform and a dynamic tunneling algorithm. The radical recognition rate is 96.5 percent correct (writer-independent) on 280,000 characters containing 98 radical classes.     

Handwritten numeral recognition using gradient and curvature of gray scale image

In this paper, the authors study on the use of gradient and curvature of the gray scale character image to improve the accuracy of handwritten numeral recognition. Three procedures, based on curvature coefficient, bi-quadratic interpolation and gradient vector interpolation, are proposed for calculating the curvature of the equi-gray scale curves of an input image. Then two procedures to compose a feature vector of the gradient and the curvature are described. The efficiency of the feature vectors are tested by recognition experiments for the handwritten numeral database IPTP CDROM1 and NIST SD3 and SD7. The experimental results show the usefulness of the curvature feature and recognition rate of 99.49% and 98.25%, which are one of the highest rates ever reported for these databases (H. Kato et al., Technical Report of IEICE, PRU95-3, 1995, p. 17; R.A. Wilkinson et al., Technical Report NISTIR 4912, August 1992; J. Geist et al., Technical Report NISTIR 5452, June 1994), are achieved, respectively.     

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.     

Handwritten character recognition by parallel labelling and shape analysis

This paper introduces a shape analysis method for handwritten characters based on polygonal approximation and a recognition on the basis of parallel fuzzy labelling. At first the input character is preprocessed for pixel definition, thinning, and tail-removal, and finally it is fed into the feature extraction and character recognition stages.     

改进SURF算法在图像汉字识别中的应用

针对复杂背景下汉字匹配准确率较低的问题,提出一种改进的SURF算法。该算法利用灰度分级的字符分割方法,先进行灰度分割增强图像的对比度,采用灰度分级树将图像中的所有像素处理为树的模式进行计算,根据灰度分级确定主节点,根据主节点的级别所对应的灰度值对图像进行分割。同时,根据汉字结构的特殊性,取消了SURF算法的旋转不变性。实验结果表明,与未使用改进的SURF算法相比,对图像质量较差的文本图像,改进的SURF算法能有效地提高其匹配的准确率。     

基于二维主成分分析与卷积神经网络的手写体汉字识别

随着计算能力的飞速增长、训练数据的不断积累以及非线性激活函数的不断完善,卷积神经网络（CNN）在手写体汉字识别中表现出较好的识别性能。针对CNN识别手写体汉字识别速度慢的问题,将二维主成分分析（2DPCA）与CNN相结合识别手写体汉字。首先,利用2DPCA提取手写体汉字的投影特征向量;然后,将得到的投影特征向量组成特征矩阵;其次,用组成的特征矩阵作为CNN的输入;最后,用Softmax函数进行分类。与基于AlexNet的CNN模型相比,所提方法的运行时间降低了78%,与基于ACNN与DCNN的模型相比,所提方法的运行时间分别降低了80%与73%。实验结果表明,该方法在不降低识别精度的同时,可以减少识别手写体汉字的运行时间。