多字体多字号印刷汉字识别方法的研究

本文对多体多字号印别汉字识别的方法进行了研究, 本文提出的方法是首先对不同字号印刷 汉字进行归一化处理, 再抽取汉字四周笔端数特征、改进粗外围特征、笔划穿插次数特征和投影变换特征, 然后对组合特征进行多级分类识别。实验在IBM一PC AT 微型机上进行, 结果表明, 实验系统在识别实际印别文本时识别率大于98%。     

一种基于BP神经网络的车牌字符分类识别方法

目前,车牌字符识别算法主要是基于模板匹配、特征匹配或神经网络的方法。本文根据车牌字符的特殊性,提出一种采用特征提取与BP神经网络学习算法相结合的分类识别技术,选取字符的粗网格特征作为字符的识别特征,以改进后的归一化字符原始特征直接输入到BP神经网络分类器中进行车牌字符识别研究。对于易混淆和相似的字符、汉字笔划粘连、字符偏移现象等都提出了自己的解决方法。实验结果说明,本方法可大幅提高车牌识别系统的正确识别率和抗干扰能力。     

A novel stroke-based feature extraction for handwritten Chinese character recognition

A stroke-based approach to extract skeletons and structural features for handwritten Chinese character recognition is proposed. We first determine stroke directions based on the directional run-length information of binary character patterns. According to the stroke directions and their adjacent relationships, we split strokes into stroke and fork segments, and then extract the skeletons of the stroke segments called skeleton segments. After all skeleton segments are extracted, fork segments are processed to find the fork points and fork degrees. Skeleton segments that touch a fork segment are connected at the fork point, and all connected skeleton segments form the character skeleton. According to the extracted skeletons and fork points, we can extract primitive strokes and stroke direction maps for recognition. A simple classifier based on the stroke direction map is presented to recognize regular and rotated characters to verify the ability of the proposed feature extraction for handwritten Chinese character recognition. Several experiments are carried out, and the experimental results show that the proposed approach can easily and effectively extract skeletons and structural features, and works well for handwritten Chinese character recognition.     

一种新的车牌字符快速识别方法

针对车牌字符图像的特点,在研究各种特征提取方法适用场合的基础上提出了改进的特征提取方法和字符识别方法。对于英文和数字,使用一种基于模板匹配和神经网络的车牌字符识别方法。该方法采用两级识别,第一级采用模板匹配识别差别明显的字符,第二级采用BP神经网络识别第一级不能确定的相似字符。对于汉字采用小波变换和LDA提取特征。该方法利用小波变换的特性最大程度地提取了字符图像的特征信息。实验结果表明此算法具有较高的识别率和较快的识别速度。     

基于手写体汉字笔画提取重构的识别方法

提出了手写体汉字笔画宽度提取、基于提取出的笔画宽度归一化手写体汉字的方法，给出手写体汉字笔画重构的思想，实现了一种基于手写体汉字笔画提取的汉字重构并最终识别手写体汉字的算法，构建了手写体汉字的识别系统。实验证实，该方法可保证原有笔画特征信息，且能有效地识别手写体汉字。     

Off-line handwritten Korean character recognition based on stroke extraction and representation

In this paper, we propose an off-line recognition method for handwritten Korean characters based on stroke extraction and representation. To recognize handwritten Korean characters, it is required to extract strokes and stroke sequence to describe an input of two-dimensional character as one-dimensional representation. We define 28 primitive strokes to represent characters and introduce 300 stroke separation rules to extract proper strokes from Korean characters. To find a stroke sequence, we use stroke code and stroke relationship between consecutive strokes. The input characters are recognized by using character recognition trees. The proposed method has been tested for the most frequently used 1000 characters by 400 different writers and showed recognition rate of 94.3%.     

基于统计与神经元方法相结合的手写体相似字识别

本文提出了一种基于统计识别方法与人工神经元网络相结合的手写体相似汉字识别方法。该方法充分利用了统计识别方法和神经元网络识别方法的优点,不仅显著地提高了相似字的识别率,而且有效地提高了系统的整体性能。对相似字的识别率由79.02%提高到84.32% ,提高了五个百分点,整体识别率提高了1.3个百分点。     

Off-line recognition of Chinese handwriting by multifeature andmultilevel classification

In this paper, an off-line recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates     

A deep learning based bank card detection and recognition method in complex scenes

With the widespread use of mobile Internet, mobile payment has become a part of daily life, and bank card recognition in natural scenes has become a hot topic. Although printed character recognition has achieved remarkable success in recent years, bank card recognition is not limited to traditional printed character recognition. There are two types of bank cards: unembossed bank cards, such as most debit cards which usually use printed characters, and embossed bank cards, such as most credit cards which mainly use raised characters. Recognition of raised characters is very challenging due to its own characteristics, and there is a lack of fast and good methods to handle it. To better recognize raised characters, we propose an effective method based on deep learning to detect and recognize bank cards in complex natural scenes. The method can accurately recognize the card number characters on embossed and unembossed bank cards. First, to break the limitation that YOLOv3 algorithm is usually used for object detection, we propose a novel approach that enables YOLOv3 to be used not only for bank card detection and classification, but also for character recognition. The CANNYLINES algorithm is used for rectification and the Scharr operator is introduced to locate the card number region. The proposed method can satisfy bank card detection, classification and character recognition in complex natural scenes, such as complex backgrounds, distorted card surfaces, uneven illumination, and characters with the same or similar color to the background. To further improve the recognition accuracy, a printed character recognition model based on ResNet-32 is proposed for the unembossed bank cards. According to the color and morphological characteristics of embossed bank cards, raised character recognition model combining traditional morphological methods and LeNet-5 convolutional neural network is proposed for the embossed bank cards. The experimental results on the collected bank card dataset and bank card number dataset show that our proposed method can effectively detect and identify different types of bank cards. The accuracy of the detection and classification of bank cards reaches 100%. The accuracy of the raised characters recognition on the embossed bank card is 99.31%, and the accuracy of the printed characters recognition on the unembossed bank card reaches 100%.

     

基于弹性网格模糊特征的手写体汉字识别方法

网格方向特征在手写体汉字识别系统中得到广泛应用,被认为是目前较成熟的手写体汉字特征之一。网格技术是网格方向特征的关键技术之一。根据汉字笔画分布特点及拓扑结构的相关性,提出了一种新的基于弹性网格及其相关模糊特征的提取方法。该方法使特征向量的信息量增加,特征更加稳定。对银行支票图像大写金额的识别率达到97.64%,实验结果证明本文方法比其他网格方向特征更有效。     

印刷表格文本分析识别系统的研究

本文介绍了一个印刷表格文本分析识别系统。提出了表格特征点分析方法。在表格图象处理的基础上, 对表格线进行分析, 在考虑表格线和字符块粘连的情况下提取字符块, 判别汉字串和数英串后分别识别, 生成表格。实验表明本方法的有效性。     

脱机印刷体彝族文字识别系统的原理与实现

脱机印刷体彝文文字识别系统包括字符分割、特征提取、特征压缩以及字典匹配四个主要模块,该系统利用总结出的彝文字符合并和反合并规则提高了字符分割准确率,采用1024维周边方向贡献度作为彝文字符统计特征,对彝文中存在的大量相似字符具有良好的区分能力。系统还采用基于KL变换的特征压缩算法和三级字典快速匹配算法,最终实现了一个基于Windows平台的脱机印刷体彝文识别平台,该平台对样本的一次识别率在99.4%以上。实验结果表明这些方法是可行的和高效的。     

基于小波变换的图像汉字笔划特征提取方法

借鉴仿生模式识别的认知观点,从汉字的构造机理和人类认识汉字的习惯角度出发,提出一种基于小波变换的图像汉字识别方法。制定了图像汉字笔划特征提取的具体规则,采用小波变换的方法对图像汉字边缘和笔划轮廓进行检测,通过有效提取图像汉字笔段信息,进行笔段合成,生成汉字或汉字的基本笔划。仿真实验结果表明,这种方法提高了图像汉字笔划特征提取的准确率和稳定性,对于印刷体和书写较规范的手写体图像汉字具有极高的识别率。     

多字体印刷藏文字符识别

藏文字符识别系统是中文多文种信息处理系统的重要组成部分,但至今国内外的研究基本处于空白。本文提出了一种基于统计模式识别的多字体印刷藏文字符识别方法:从字符轮廓中抽取方向线素特征,利用线性鉴别分析(LDA)压缩降维后得到紧凑的字符特征向量。采用基于置信度分析的两级分类策略,设计了带偏差欧氏距离分类器(EDD)完成高效的粗分类,细分类采用修正二次鉴别函数(MQDF)。通过实验选取恰当的分类器参数后,在容量为177,600字符(300样本/字符类)的测试集上的识别率达到99.79%,证明了该方法的有效性。     

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。     

汉字笔段形成规律及其提取方法

该文从点阵图像行(列)连通像素段出发，研究汉字图像的笔段构成，发现汉字点阵图像仅由阶梯型笔段和平行长笔段两种类型的笔段构成，并归纳出阶梯型笔段和平行长笔段的形成规律．以笔段形成规律为基础提出了汉字笔段的提取方法，该方法将像素级汉字图像转变为以笔段为单位的图像，有利于汉字识别、汉字细化及汉字字体的自动生成．最后该文给出了印刷体和手写体汉字笔段提取的实验结果．     

Off-line handwritten Chinese character recognition as a compoundBayes decision problem

A handwritten Chinese character off-line recognizer based on contextual vector quantization (CVQ) of every pixel of an unknown character image has been constructed. Each template character is represented by a codebook. When an unknown image is matched against a template character, each pixel of the image is quantized according to the associated codebook by considering not just the feature vector observed at each pixel, but those observed at its neighbors and their quantization as well. Structural information such as stroke counts observed at each pixel are captured to form a cellular feature vector. Supporting a vocabulary of 4616 simplified Chinese characters and alphanumeric and punctuation symbols, the writer-independent recognizer has an average recognition rate of 77.2 percent. Three statistical language models for postprocessing have been studied for their effectiveness in upgrading the recognition rate of the system. Among them, the CVQ-based language model is the most effective one upgrading the recognition rate by 10.4 percent on the average     

基于汉字连通分量的印刷图像版面分割方法

针对彩色印刷图像背景色彩丰富和汉字存在多个连通分量,连通域文字分割算法不能精确提取文字,提出基于汉字连通分量的彩色印刷图像版面分割方法。利用金字塔变换逆半调算法对图像进行预处理,通过颜色采样和均值偏移分割图像颜色,标记文字连通分量,根据汉字结构和连通分量特性重建汉字连通分量,分析文字连通分量连接关系确定文字排列方向实现文字分割。实验结果表明,该方法能够有效地重建汉字连通分量,在彩色印刷图像中实现对不同字体、字号、颜色的文字分割。     

A stroke-based neuro-fuzzy system for handwritten chinese character recognition

In this article, a stroke-based neuro-fuzzy system for off-line recognition of handwritten Chinese characters is proposed. The system consists of three main components: stroke extraction, feature extraction, and recognition. Stroke extraction applies a run-length-based method to extract strokes from the image of a given character. Various fuzzy features of the extracted strokes, including slope, length, location, and cross relation, are obtained by the feature extraction module. An ART-based neural network, using a two-stage training algorithm, is used to recognize characters. This system extracts strokes in only two passes, and is free from the presence of spurious and thick strokes. The neural model used provides a fast convergence rate. Nodes are allowed to be shared to reduce the size of the resulting network. Features need not be classified in advance by the user. Furthermore, the architecture of the network is self-constructed without the intervention of the user. Experiments have shown that this system is effective.     

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.