基于机器视觉的印刷标签检测系统的改进

针对图像采集的非同步性和传送平台存在的抖动等因素造成采集图像质量降低的问题,提出了一种简单可行的高可靠性机器视觉印刷标签检测系统。通过比较几种边缘检测算法,采用Canny这种相对高可靠性的边缘检测算法制作边缘掩膜,通过在模板图像上加盖边缘掩膜,在差影比较后对差影图像进行形态学去噪来消除轮廓伪影和人眼难以识别的微小缺陷。该方法运用在印刷标签质量检测系统中,有效地降低了印刷标签误检率,并且符合人眼识别特性。     

基于篇章字体导引的单字符字体识别

字体识别是印刷文档识别重构的重要组成部分,是目前识别技术的一个难题。以印刷文档的单体单字高识别率为基础,论文提出了一种基于篇章字体导引的汉字单字符字体识别方法,结合字体排版的规律性,使得字体识别速度和精度大大提高。以常用报纸、杂志正文文本为样本进行实验,字体识别率达到了99%。     

基于条码扫描器和ATmega16的五线谱识别发音设备

五线谱是目前应用最为广泛的音乐记录工具,阅读五线谱是所有音乐爱好者和专业人员必备的技能。基于激光条码扫描器制作出了一种能够识别印刷五线谱的识别发音设备。经过实测,对印刷五线谱音符的识别准确率高,发音精确,使用效果良好。该识别发音设备既可用于娱乐、教学,也可以进行进一步的商业开发。     

介绍几台光学符号识别装置

(一)采用复合类似度的ASPET/71 对主要用于电子计算机输入设备的印刷符号识别装置来说,要求有很高的识别性能,例如对拒绝率要求在10~(-5)以下,误读率要求在10~(-7)以下,因此如果不对输入符号的质量加很严格的限制,就不可能实现上述的要求。目前,符号识别的研究方向是研制在这种严格条件下,能读出尽量多的符号和字体,也能读出印刷质量差的符号的方法。在印刷符号的情况下,因为可以预先确定所处理的符号的形状,所以大多采用重合法,根据未知图形同基准符号模型之间的重合程度,将最相似的符号作为识别结果。     

印刷维吾尔文特征提取之方向码

特征提取方法的选择是影响识别率的一个至关重要的因素。而印刷识别中的分类特征很多,让每一类特征具有良好的分类能力和稳定性是仍需要解决的问题。现就以具有高稳定性和抗干扰能力的八方向码特征为例,对其提取方法进行了全面的阐述。     

AdaBoost算法在喷码图像识别的应用

喷码在印刷中使用比较普遍,其识别通常采用模板匹配的方法,但是由于喷码常出现误差,为模板匹配方法识别带来难度。AdaBoost是一个建构准确分类器的学习算法,文中将此算法应用于喷码图像的识别,不仅提高了识别的准确率,速度也更为理想。     

多体印刷英文识别系统的算法设计

本文研究介绍了一套基于最小距离分类器的多体印刷英文识别系统的算法,详细阐述了印刷英文资料中字切分与行切分的具体方案,建立了以马氏距离为距离度量的最小距离分类器以实现对于印刷英文资料的识别处理,最后借助基于英文词典和识别置信度加权编辑距离后处理规则对识别结果加以完善,进一步提高多体印刷英文识别算法的准确率水平。     

印刷

在盘片的非读出面印刷标签,主要原因有二:识别盘片、增强盘片外在的视觉体现。无论是功能上的需要还是盘片装饰的需要,标签印刷都采用分离的离线工艺进行。在盘片上印刷图像的技术有很多选择。光盘自动生产工艺结束后,盘片存放在串轴上,通常被人工移走,放到小推车上然后送到印刷部门。取决于工厂的大小,可能有一台或几台印刷机,紧邻生产线或位于独立的车间。印刷工艺发展到现在,重新又回到了离线方式。在早期采用批量生产技术,随后集成生产线流行起来,而印刷就是生产线上的最后一道工艺。初期,在线式的印刷意义很大,但是随着模压周期越来越短,而印刷速度又相对较低,印刷工艺逐渐成为在线生产的瓶颈,影响整条线的生产周期。离线印刷还具有灵活的优势,可以选择不同的印刷机,也可选择不同的印刷方式。     

一个面向OA的印刷汉字OCR实用系统

本文叙述一个采取以“统计模式识别”为主, 以“结构模式识别”方法为辅的识别技术路线实现的以办公室自动化（OA）为应用环境的一级印刷汉字文本识别系统，该系统从实用化角度出发, 采用页式文本图象扫描输入，输入后将图象文本分割成单个汉字, 并根据汉字的结构特点, 抽取了汉字的内层, 外层,局部等多个特征。识别采用多级分类方法。识别结果形成一个国标区位码文件，系统软件建立了一种与用户间的友好界面。该系统是在IBM PC/XT上实现的, 对印刷字样识别率>99%, 对各类实际的办公行文其统计识别率>95%, 识别速度为1-2字/秒。 前     

汉神文字识别功能简介

汉神汉字识别(HansReader 5.0),是印刷体中英文快速录入的实用工具。对于一份已经排版并印刷好文稿,如:报章、公文、书刊等,如要节选其中一段或全部,就可使用印刷体汉字识别软件,不必浪费人力采用键盘输入。汉神文字识别软件,能准确快速地将汉字图像文件识别为汉字文本文件。     

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.     

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%.

     

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。     

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.     

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。     

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.     

Automated entry system for Chinese printed documents

In this paper, we present a new automated Chinese printed document entry system. This system features automated text/ graph segmentation, and multi-font, multi-size printed Chinese character recognition. Experimental results show that 95.8–99.4% of the top 10 printed characters can be correctly recognized, with the speed of 0.16 seconds/character.