Character recognition without segmentation

A segmentation-free approach to OCR is presented as part of a knowledge-based word interpretation model. It is based on the recognition of subgraphs homeomorphic to previously defined prototypes of characters. Gaps are identified as potential parts of characters by implementing a variant of the notion of relative neighborhood used in computational perception. Each subgraph of strokes that matches a previously defined character prototype is recognized anywhere in the word even if it corresponds to a broken character or to a character touching another one. The characters are detected in the order defined by the matching quality. Each subgraph that is recognized is introduced as a node in a directed net that compiles different alternatives of interpretation of the features in the feature graph. A path in the net represents a consistent succession of characters. A final search for the optimal path under certain criteria gives the best interpretation of the word features. Broken characters are recognized by looking for gaps between features that may be interpreted as part of a character. Touching characters are recognized because the matching allows nonmatched adjacent strokes. The recognition results for over 24,000 printed numeral characters belonging to a USPS database and on some hand-printed words confirmed the method's high robustness level     

Recognition of hand-printed Chinese characters using decision trees/machine learning C4.5 system

Recognition of Chinese characters has been an area of major interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This paper presents a new technique for the recognition of hand-printed Chinese characters using the C4.5 machine learning system. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The paper discusses Chinese character recognition using theHough transform for feature extraction and C4.5 system. The system was tested with 900 characters written by different writers from poor to acceptable quality (each character has 40 samples) and the rate of recognition obtained was 84%.     

多字体印刷维吾尔文的切分

在许多文字识别系统中, 字符切分是预处理阶段的一部分, 其目的是从文本图象中分离出字母图象。而后才能针对切分后的每个字母进行识别。在具有连体特征的文字中, 字符切分就显得特别重要, 因为字符切分的准确与否直接影响字符的识别。维吾尔文就具有这种明显的连体特点, 本文主要讨论了采用抽取投影特征的方法, 实现了多字体维吾尔文的行切分、字切分和字符切分。     

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%.

     

An on-line character recognition aimed at a substitution for a billing machine keyboard

An on-line character recognition system was developed which recognized small sized characters, whose typical height was 4 mm, with a recognition rate of 98%. The system features programming flexibility and modifying a recognition logic. It was made possible by the scheme whereby proposition-test sequences were separated into both an assembly of tree node data and test subroutines. A demonstration system was also developed which enters personal data into a computer by recognizing hand-printed characters. The whole system showed a feasible substitution for a billing machine keyboard in data entry applications.     

Feature Point Method of Chinese Character Recognition and Its Application

A new method for recognizing Chinese characters is proposed. It is based on the so-called featurepoints of Chinese characters. The feature points we use include those on the stroke of a character, i.e., endpoints, turning points, fork points and cross points, and the key points on the background of character. Thismethod differs from the previous ones for it combines the feature points on stroke with those on back-ground and it uses feature points to recognize Chinese characters directly. A Chinese character recognitionsystem based on top-down dynamical matching of feature point is developed. The system can recognizenot only 6763 printed sample Song font Chinese characters of size 5.6×5.6mm~2 with high recognition rate,but also the general printed books, magazines and documents with a satisfactory recognition rate andspeed.     

Character pattern extraction from documents with complex backgrounds

Recent remarkable progress in computer systems and printing devices has made it easier to produce printed documents with various designs. Text characters are often printed on colored backgrounds, and sometimes on complex backgrounds such as photographs, computer graphics, etc. Some methods have been developed for character pattern extraction from document images and scene images with complex backgrounds. However, the previous methods are suitable only for extracting rather large characters, and the processes often fail to extract small characters with thin strokes. This paper proposes a new method by which character patterns can be extracted from document images with complex backgrounds. The method is based on local multilevel thresholding and pixel labeling, and region growing. This framework is very useful for extracting character patterns from badly illuminated document images. The performance of extracting small character patterns has been improved by suppressing the influence of mixed-color pixels around character edges. Experimental results show that the method is capable of extracting very small character patterns from main text blocks in various documents, separating characters and complex backgrounds, as long as the thickness of the character strokes is more than about 1.5 pixels. Received July 23, 2001 / Accepted November 5, 2001     

一个基于骨架汉字技术的字形设计与显示系统SCCDS

介绍了一个基于骨架汉字技术的字形设计与显示系统SCCDS。利用骨架汉字数据结构的灵活性, 该系 统能方便地进行字形的交互输入和修改。     

Recognition of Bangla compound characters using structural decomposition

In this paper we propose a novel character recognition method for Bangla compound characters. Accurate recognition of compound characters is a difficult problem due to their complex shapes. Our strategy is to decompose a compound character into skeletal segments. The compound character is then recognized by extracting the convex shape primitives and using a template matching scheme. The novelty of our approach lies in the formulation of appropriate rules of character decomposition for segmenting the character skeleton into stroke segments and then grouping them for extraction of meaningful shape components. Our technique is applicable to both printed and handwritten characters. The proposed method performs well for complex-shaped compound characters, which were confusing to the existing methods.     

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。     

On a pattern recognition method based on polynomial regression

Consideration is given to theoretical bases and aspects of the practical application of a method of pattern recognition of printed and hand-printed symbols, which relies on the polynomial regression. Characteristics are described of the quality of the program implementation of the method, which are defined on the bases of graphic patterns of symbols with known bounds. The correlation is made with characteristics of the known symbol recognition algorithms, such as neural networks and an algorithm of the comparison with reference patterns.     

神经网络在车辆牌照字符识别中的应用

在车辆牌照自动识别系统中,因自然因素或采用因素使得原本原则的印刷字符产生畸变,给字符识别带来了很大困难。本文在特征抽取的基础上,采用ＢＰ网络进行分类,并附加线性感知器来实现单字的有效识别。该方法算法简便,识别率高,可适用于多种高噪声环境中的印刷字体识别。     

An improved contour-based thinning method for character images

Digital skeleton of character images, generated by thinning method, has a wide range of applications for shape analysis and classification. But thinning of character images is a big challenge. Removal of spurious strokes or deformities in thinning is a difficult problem. In this paper, we propose a contour-based thinning method used for performing skeletonization of printed noisy isolated character images. In this method, we use shape characteristics of text to get skeleton of nearly same as the true character shape. This approach helps to preserve the local features and true shapes of the character images. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. Experiment is done on printed English, Bengali, Hindi, and Tamil characters and we obtain much better results comparing with other thinning methods without any post-processing.     

复杂背景图像的字符识别算法研究

为了解决字符识别过程中的局部曝光、印刷字符的断裂以及变形和自然环境下的背景污染等问题, 提出了一种分块处理与卷积神经网络(CNN)相结合的字符图像识别算法. 首先利用OpenCV机器视觉库, 结合分块处理、伽马运算、参数调整等方法对产品零件表面印刷字符进行预处理, 初步解决图像局部曝光和字符断裂问题; 其次为了获得单个字符图像, 利用数学形态学算法对局部曝光处理后的二值化图像进行分步分割, 进而去掉字符间的无用信息; 最后利用Keras模块为字符识别提供的API搭建CNN模型, 经过对100多张字符的识别训练, 准确率高达96.9%, 为某汽车零部件自动化生产中的字符识别提供了可靠的依据.     

Binarization using associative addressing

For use in hand-printed character recognition, this paper introduces a method of binarization that can cope with quite large and abrupt changes of the grey levels of black and white. The binarizing threshold is a piece-wise linear function of a local maximum white level, and this function is conveniently implemented by means of associative addressing.     

印刷字符在线检测的预处理算法及试验研究*

根据票证印刷图像的特点, 提出了采用基于数学形态学的定向补全算法来解决运动模糊消除问题, 并采用最佳阈值分割算法来得到二值化图像以及采用轮廓投影切分算法进行字符区域分割, 进而完成了对在线采集印刷字符运动图像的预处理过程。同时, 利用VC ++6.0 实现了该算法, 并进行了相应的试验工作。