英文字符特征提取系统

英文字符识别是模式识别的一个重要分支,具有广泛的应用领域.字符识别主要包括文档切分、单词切分、字符识别及后处理几部分.文中描述的是英文字符识别系统实现了从图像扫描到得到识别结果的全过程, 而字符特征提取是文本的重点内容.以五十二个英文字符为研究对象,具体包括了图像预处理、特征提取、建立模板、分类器设计、后处理等步骤.文章对OCR领域中应用比较广泛的网格特征、外围特征、穿越特征等特征和几种距离分类器分别进行比较分析,并进行大量的实验.实验结果表明识别准确率和识别处理时间方面具有良好性能.     

KNN-均值算法

基于传统KNN的弱点．提出一种新的改进方法：在训练样本中取与待测样本最近邻的K个样本;把这K个样本按类划分取每类的平均样本;计算每个类的平均样本与待测样本的欧氏距离,并将样本归为距离最小的那一类。在南京理工大学NUST603HW手写体汉字库以及Concordia大学的CENPARMI手写体阿拉伯数字数据库上的试验结果表明,新方法较传统KNN的识别率有明显提高。     

藏文识别的预处理

预处理是整个文字识别系统的重要组成部分,预处理性能的优劣将直接影响整个识别系统的性能,根据藏文字在字形和书写方式上的特点,实现了一种适用于藏文识别的预处理技术,整个预处理过程包括二值化、版面分析、倾斜校正、字符切和归一化,在预处理过程中还提取了一此圾关字丁的基本特征,这些特征充分反映了藏文的特点,具有良好的稳定性,可以用于识别系统的粗分类和后处理。     

Partial discriminative training for classification of overlapping classes in document analysis

For character recognition in document analysis, some classes are closely overlapped but are not necessarily to be separated before contextual information is exploited. For classification of such overlapping classes, either discriminating between them or merging them into a metaclass does not satisfy. Merging the overlapping classes into a metaclass implies that within-metaclass substitution is considered as correct classification. For such classification problems, this paper proposes a partial discriminative training (PDT) scheme, in which, a training pattern of an overlapping class is used as a positive sample of its labeled class, and neither positive nor negative sample for its allied classes (those overlapping with the labeled class). In experiments of offline handwritten letter and online symbol recognition using various classifiers evaluated at metaclass level, the PDT scheme mostly outperforms ordinary discriminative training and merged metaclass classification. This work was supported by the Hundred Talents Program of Chinese Academy of Sciences and the National Natural Science Foundation of China (NSFC) under Grants No. 60775004 and No. 60723005.     

Performance improvement of character recognition in industrial applications using prior knowledge for more reliable segmentation

In industrial applications optical character recognition with smart cameras becomes more and more popular. Since these applications mostly have challenging environments for the systems it is most important to have very reliable character segmentation and classification algorithms. The investigations of several algorithms have shown that character segmentation is one if not the main bottleneck of character recognition. Furthermore, the requirements of robust and fast algorithms related to skew angle estimation and line segmentation, as well as tilt angle estimation, and character segmentation are high. This is the reason for introducing such algorithms that are specifically adapted to industrial applications. Additionally, a method is proposed that is based on the Bayes theorem to take account of prior knowledge for line and character segmentation. The main focus of the investigations of the character recognition system is recognition performance and speed, since real-time constraints are very hard in industrial application. Both requirements are evaluated on an image series captured with a smart camera in an industrial application.     

Some experiments in the evaluation of a character recognition scanner

For character recognition machines in which the design of the logic is time consuming and expensive, a method for evaluating the character recognition scanner prior to designing the recognition logic is desirable. A method for evaluating a character recognition scanner by evaluating the patterns it produces is described. The method, for a given set of parameters, assigns to each pattern a figure of merit that measures the difficulty of recognizing the pattern. A number of experiments show that the evaluation method produced results comparable to results produced by a recognition logic designed by engineers. On a sample of 81,200 patterns, the evaluation method showed in this experiment that 0.20% of the patterns would be difficult to recognize, while for the same patterns, logic designed by engineers failed to recognize or misrecognized 0.24% of the patterns.     

Towards a Multinational Car License Plate Recognition System

A full-fledged image-based car license plate recognition (CLPR) system is described in the paper. CLPR provides an inexpensive automatic solution for remote vehicle identification. Gray-level input images are assumed. The localization stage of the CLPR yields a plate clip followed by character segmentation and recognition. The recognition scheme combines adaptive iterative thresholding with a template-matching algorithm. The method is invariant to illumination and is robust to character size and thickness, skew and small character breaks. Promising results have been obtained in the experiments with Israeli and Bulgarian license plates including images of poor quality. Also, the possibility of using an “off-the-shelf” OCR has been explored.     

基于字符分割和LeNet-5网络的字符验证码识别

为了解决传统验证码识别方法效率低,精度差的问题,设计了一种先分割后识别的验证码处理方案。该方案在预处理阶段用中值滤波去噪,再利用霍夫变换对图像字符进行矫正;在字符分割阶段,利用垂直投影算法确定验证码字符块个数,以及字符坐标点,再用颜色填充算法对验证码进行初步分割,根据分割后的字符块数量对粘连字符进行二次分割;在识别阶段,我们对LeNet-5网络进行了改进,修改了输入层,并用全连接层替换了LeNet-5网络中的C5层,以此来对验证码字符进行识别;实验表明,对于非粘连验证码和粘连验证码,单张图片分割时间为0.14和0.15ms,分割准确率为98.75%和97.25%,识别准确率为99.99%和97.7%;结果表明,该算法对验证码分割和识别都有着很好的效果。     

An automated vision system for container-code recognition

Automatic container-code recognition is of great importance to the modern container management system. Similar techniques have been proposed for vehicle license plate recognition in past decades. Compared with license plate recognition, automatic container-code recognition faces more challenges due to the severity of nonuniform illumination and invalidation of color information. In this paper, a computer vision based container-code recognition technique is proposed. The system consists of three function modules, namely location, isolation, and character recognition. In location module, we propose a text-line region location algorithm, which takes into account the characteristics of single character as well as the spatial relationship between successive characters. This module locates the text-line regions by using a horizontal high-pass filter and scanline analysis. To resolve nonuniform illumination, a two-step procedure is applied to segment container-code characters, and a projection process is adopted to isolate characters in the isolation module. In character recognition module, the character recognition is achieved by classifying the extracted features, which represent the character image, with trained support vector machines (SVMs). The experimental results demonstrate the efficiency and effectiveness of the proposed technique for practical usage.     

分时电表表盘数字读数的自动识别系统

研究了分时电表表盘数字的自动识别，它是电表误差分析系统的关键所在。识别工作包括平段和谷段整数和小数数字的识别，难点在于刻度轴歪斜、半个字符的识别，以及刻度对应的小数值的推算。该文构建了一个完整的分时电表表盘数字自动识别系统。该系统以二值化、投影算法、开闭运算、边缘提取、Hough变换等一系列算法作为预处理，提出了整字和非整字两种识别算法。针对300帧现场采集的电表图像，该系统获得了单字正确识别率998％和整表正确识别率97．3％的结果，表明该系统具有较强的鲁棒性。     

On-line computer recognition of proposed standard ANSI(USASI) handprinted characters

A robust real-time system for recognition of handprinted characters of the upper case English alphabet is described. The basic system is suited to implementation on small computers and has been designed to accept characters conforming to the stroke types and sequences suggested by a proposed ANSI(USASI) standard. Experiments with 2340 samples from 10 untrained subjects yielded an overall character recognition accuracy of 98.3%. The system is quite robust with respect to size and stylistic variations. The robustness and real-time operation of the system are largely attributed to the preprocessing and stroke identification techniques developed, which include a new two-stage syntactic classifier for the identification of curvilinear strokes.     

工业器件上的字符提取及识别

介绍了一个工业器件上字符识别和获取系统.在字符提取阶段,给出了两种字符的提取方法以及互补Ostu二值化方法.在识别阶段,对第一类字符利用TM 矩和仿射不变矩提取特征并且组合成特征向量,然后利用交叉相关来识别; 对第二类字符利用模板匹配法,基于Hu不变矩和TM 矩的交叉相关分别得到各自的识别结果,然后采用投票原则来作出最后的判决.实验结果表明提出的方案是可行的和合理的.     

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.     

一种字符定位及识别技术在交通领域的应用

字符定位与识别技术在交通领域应用广泛.字符识别系统包括字符图像的拍摄、预先处理,将字符从图中截取出来,最后对字符对比甄别.采用灰阶处理、均值滤波等方法对图像预处理.在字符定位部分,采用边缘检测算法对字符图像在原始图中位置进行定位.在字符分割前,对字符进行二值化及倾斜校正.利用区域增长(region growing)方法...     

基于VC^＋＋的车牌识别系统关键技术研究

车辆牌照识别（LPR）系统是一个专用的计算机视觉系统，LPR系统的广泛应用将有助于加快我国交通管理自动化的进程。本文对车牌识别系统中的图像预处理、车牌定位、字符分割和字符识别等环节涉及到的技术、算法以及系统整体设计作了全面的论述，并与已有方案进行了比较，对部分关键算法进行了设计和改进。用采集到的40多幅汽车图片作为实验样本数据，用纯软件的方法实现了车牌字符的自动识别。     

Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters

This paper describes an adaptive recognition system for isolated handwritten characters and the experiments carried out with it. The characters used in our experiments are alphanumeric characters, including both the upper- and lower-case versions of the Latin alphabets and three Scandinavian diacriticals. The writers are allowed to use their own natural style of writing. The recognition system is based on the k-nearest neighbor rule. The six character similarity measures applied by the system are all based on dynamic time warping. The aim of the first experiments is to choose the best combination of the simple preprocessing and normalization operations and the dissimilarity measure for a multi-writer system. However, the main focus of the work is on online adaptation. The purpose of the adaptations is to turn a writer-independent system into writer-dependent and increase recognition performance. The adaptation is carried out by modifying the prototype set of the classifier according to its recognition performance and the user's writing style. The ways of adaptation include: (1) adding new prototypes; (2) inactivating confusing prototypes; and (3) reshaping existing prototypes. The reshaping algorithm is based on the Learning Vector Quantization. Four different adaptation strategies, according to which the modifications of the prototype set are performed, have been studied both offline and online. Adaptation is carried out in a self-supervised fashion during normal use and thus remains unnoticed by the user. Received June 30, 1999 / Revised September 29, 2000     

利用字形风格约束的字符识别研究

印刷体字符的字形风格和手写字符的书写风格是非常重要的特性. 本文研究了利用字符字形风格之间的约束关系提高识别率的理论和方法, 提出了以字形风格同现概率为基础的 3 种识别模型, 结合实验结果分析了这些模型的优缺点和适用条件, 结果验证了本文提出的风格约束模型能够有效地提高识别率.