作者识别与验证的不同特征提取方法综述

IF 1.1 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jaya Paul, Kalpita Dutta, Anasua Sarkar, Nibaran Das, Kaushik Roy
{"title":"作者识别与验证的不同特征提取方法综述","authors":"Jaya Paul, Kalpita Dutta, Anasua Sarkar, Nibaran Das, Kaushik Roy","doi":"10.1504/ijapr.2023.130511","DOIUrl":null,"url":null,"abstract":"Identifying and verifying a person based on scanned images of their handwriting is a needful biometric application in historical document analysis, behavioural biometrics study, forensic science, access control, graphology, and copyrights management. Writer identification and verification are still challenging in offline and online handwriting recognition. Since the performances of handwriting biometric identification and verification systems depend on both the quality and types of chosen features, this is one of the most critical phases. This article represents a literature survey on offline and online biometric features used in different scripts for writer verification and identification techniques. Several previous efficient works on online and offline writer authentication methods for biometrics using cutting-edge hand-craft features in different levels of handwriting analysis like documents, paragraphs, words, and characters are analysed systematically to date for the first time in detail.","PeriodicalId":43486,"journal":{"name":"International Journal of Applied Pattern Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":1.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A survey on different feature extraction methods for writer identification and verification\",\"authors\":\"Jaya Paul, Kalpita Dutta, Anasua Sarkar, Nibaran Das, Kaushik Roy\",\"doi\":\"10.1504/ijapr.2023.130511\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Identifying and verifying a person based on scanned images of their handwriting is a needful biometric application in historical document analysis, behavioural biometrics study, forensic science, access control, graphology, and copyrights management. Writer identification and verification are still challenging in offline and online handwriting recognition. Since the performances of handwriting biometric identification and verification systems depend on both the quality and types of chosen features, this is one of the most critical phases. This article represents a literature survey on offline and online biometric features used in different scripts for writer verification and identification techniques. Several previous efficient works on online and offline writer authentication methods for biometrics using cutting-edge hand-craft features in different levels of handwriting analysis like documents, paragraphs, words, and characters are analysed systematically to date for the first time in detail.\",\"PeriodicalId\":43486,\"journal\":{\"name\":\"International Journal of Applied Pattern Recognition\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Applied Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijapr.2023.130511\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijapr.2023.130511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 1

摘要

在历史文献分析、行为生物识别研究、法医学、访问控制、笔迹学和版权管理等领域,基于扫描的笔迹图像来识别和验证一个人是一项必要的生物识别应用。在线下和线上的手写识别中,写信人的识别和验证仍然具有挑战性。由于手写生物识别和验证系统的性能取决于所选特征的质量和类型,因此这是最关键的阶段之一。本文对不同文字中用于作者验证和识别技术的离线和在线生物特征进行了文献综述。本文首次系统地分析了迄今为止在不同层次的笔迹分析(如文档、段落、单词和字符)中使用尖端手工特征的在线和离线生物识别作者认证方法的几项高效工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A survey on different feature extraction methods for writer identification and verification
Identifying and verifying a person based on scanned images of their handwriting is a needful biometric application in historical document analysis, behavioural biometrics study, forensic science, access control, graphology, and copyrights management. Writer identification and verification are still challenging in offline and online handwriting recognition. Since the performances of handwriting biometric identification and verification systems depend on both the quality and types of chosen features, this is one of the most critical phases. This article represents a literature survey on offline and online biometric features used in different scripts for writer verification and identification techniques. Several previous efficient works on online and offline writer authentication methods for biometrics using cutting-edge hand-craft features in different levels of handwriting analysis like documents, paragraphs, words, and characters are analysed systematically to date for the first time in detail.
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
International Journal of Applied Pattern Recognition
International Journal of Applied Pattern Recognition COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
自引率
0.00%
发文量
9
×
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