NeuroPapyri: A Deep Attention Embedding Network for Handwritten Papyri Retrieval

Giuseppe De Gregorio, Simon Perrin, Rodrigo C. G. Pena, Isabelle Marthot-Santaniello, Harold Mouchère
{"title":"NeuroPapyri: A Deep Attention Embedding Network for Handwritten Papyri Retrieval","authors":"Giuseppe De Gregorio, Simon Perrin, Rodrigo C. G. Pena, Isabelle Marthot-Santaniello, Harold Mouchère","doi":"arxiv-2408.07785","DOIUrl":null,"url":null,"abstract":"The intersection of computer vision and machine learning has emerged as a\npromising avenue for advancing historical research, facilitating a more\nprofound exploration of our past. However, the application of machine learning\napproaches in historical palaeography is often met with criticism due to their\nperceived ``black box'' nature. In response to this challenge, we introduce\nNeuroPapyri, an innovative deep learning-based model specifically designed for\nthe analysis of images containing ancient Greek papyri. To address concerns\nrelated to transparency and interpretability, the model incorporates an\nattention mechanism. This attention mechanism not only enhances the model's\nperformance but also provides a visual representation of the image regions that\nsignificantly contribute to the decision-making process. Specifically\ncalibrated for processing images of papyrus documents with lines of handwritten\ntext, the model utilizes individual attention maps to inform the presence or\nabsence of specific characters in the input image. This paper presents the\nNeuroPapyri model, including its architecture and training methodology. Results\nfrom the evaluation demonstrate NeuroPapyri's efficacy in document retrieval,\nshowcasing its potential to advance the analysis of historical manuscripts.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.07785","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The intersection of computer vision and machine learning has emerged as a promising avenue for advancing historical research, facilitating a more profound exploration of our past. However, the application of machine learning approaches in historical palaeography is often met with criticism due to their perceived ``black box'' nature. In response to this challenge, we introduce NeuroPapyri, an innovative deep learning-based model specifically designed for the analysis of images containing ancient Greek papyri. To address concerns related to transparency and interpretability, the model incorporates an attention mechanism. This attention mechanism not only enhances the model's performance but also provides a visual representation of the image regions that significantly contribute to the decision-making process. Specifically calibrated for processing images of papyrus documents with lines of handwritten text, the model utilizes individual attention maps to inform the presence or absence of specific characters in the input image. This paper presents the NeuroPapyri model, including its architecture and training methodology. Results from the evaluation demonstrate NeuroPapyri's efficacy in document retrieval, showcasing its potential to advance the analysis of historical manuscripts.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
神经纸莎草纸:用于手写纸莎草纸检索的深度注意力嵌入网络
计算机视觉与机器学习的交汇已成为推动历史研究的一个重要途径,有助于对我们的过去进行更深入的探索。然而,由于其 "黑箱 "性质,机器学习方法在历史古文字学中的应用常常受到批评。为了应对这一挑战,我们推出了神经纸莎草纸,这是一种基于深度学习的创新模型,专门用于分析包含古希腊纸莎草纸的图像。为了解决与透明度和可解释性相关的问题,该模型采用了注意力机制。这种注意力机制不仅增强了模型的性能,还为对决策过程有重要贡献的图像区域提供了可视化表示。该模型专门针对处理带有手写文字行的纸莎草纸文档图像进行了校准,利用单个注意力图来告知输入图像中特定字符的存在与否。本文介绍了 NeuroPapyri 模型,包括其架构和训练方法。评估结果证明了 NeuroPapyri 在文档检索方面的功效,并展示了其在推进历史手稿分析方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Publishing Instincts: An Exploration-Exploitation Framework for Studying Academic Publishing Behavior and "Home Venues" Research Citations Building Trust in Wikipedia Evaluating the Linguistic Coverage of OpenAlex: An Assessment of Metadata Accuracy and Completeness Towards understanding evolution of science through language model series Ensuring Adherence to Standards in Experiment-Related Metadata Entered Via Spreadsheets
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1