模板匹配在光学字符识别中的应用

D. Kalina, R. Golovanov
{"title":"模板匹配在光学字符识别中的应用","authors":"D. Kalina, R. Golovanov","doi":"10.1109/EICONRUS.2019.8657297","DOIUrl":null,"url":null,"abstract":"Optical character recognition (OCR) is one of the common research problems in the computer vision which is used for industrial processes automation. In this work the algorithm of OCR based on template matching recognition is presented. Algorithm is adopted for character recognition with consideration of low contrast conditions and texture on the background. The main feature of this method is interest area detection and adaptive binarization before pattern matching. The proposed method does not require large set of training samples, which may be critical in some applications. Also algorithm is easy to implement and has low complexity. Testing was conducted on custom dataset under the various conditions of illumination and pattern readability. The quality of the algorithm was estimated using standard binary classifier metrics and the distribution of correlation function in comparison with the template.","PeriodicalId":6748,"journal":{"name":"2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)","volume":"46 1","pages":"2213-2217"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Application of Template Matching for Optical Character Recognition\",\"authors\":\"D. Kalina, R. Golovanov\",\"doi\":\"10.1109/EICONRUS.2019.8657297\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Optical character recognition (OCR) is one of the common research problems in the computer vision which is used for industrial processes automation. In this work the algorithm of OCR based on template matching recognition is presented. Algorithm is adopted for character recognition with consideration of low contrast conditions and texture on the background. The main feature of this method is interest area detection and adaptive binarization before pattern matching. The proposed method does not require large set of training samples, which may be critical in some applications. Also algorithm is easy to implement and has low complexity. Testing was conducted on custom dataset under the various conditions of illumination and pattern readability. The quality of the algorithm was estimated using standard binary classifier metrics and the distribution of correlation function in comparison with the template.\",\"PeriodicalId\":6748,\"journal\":{\"name\":\"2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)\",\"volume\":\"46 1\",\"pages\":\"2213-2217\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EICONRUS.2019.8657297\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EICONRUS.2019.8657297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

光学字符识别(OCR)是应用于工业过程自动化的计算机视觉中常见的研究问题之一。本文提出了基于模板匹配识别的OCR算法。该算法在考虑低对比度条件和背景纹理的情况下进行字符识别。该方法的主要特点是在模式匹配前进行兴趣区域检测和自适应二值化。该方法不需要大量的训练样本,这在某些应用中可能是至关重要的。该算法易于实现,复杂度低。在自定义数据集上进行了不同光照和模式可读性条件下的测试。采用标准二分类器度量和相关函数的分布与模板进行比较,对算法的质量进行了估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Template Matching for Optical Character Recognition
Optical character recognition (OCR) is one of the common research problems in the computer vision which is used for industrial processes automation. In this work the algorithm of OCR based on template matching recognition is presented. Algorithm is adopted for character recognition with consideration of low contrast conditions and texture on the background. The main feature of this method is interest area detection and adaptive binarization before pattern matching. The proposed method does not require large set of training samples, which may be critical in some applications. Also algorithm is easy to implement and has low complexity. Testing was conducted on custom dataset under the various conditions of illumination and pattern readability. The quality of the algorithm was estimated using standard binary classifier metrics and the distribution of correlation function in comparison with the template.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信