Tsinghua Bamboo Slip Scribe Verification Using Siamese Networks

Tsinghua Bamboo Slip Scribe Verification Using Siamese Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Tsinghua Bamboo Slip Scribe Verification Using Siamese Networks Haiyang Wang, Mingjun Li, Bowen Liu, Yangchen Guo, Yanbo Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7217961/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2026 Read the published version in npj Heritage Science → Version 1 posted 9 You are reading this latest preprint version Abstract The exploration of bamboo slip manuscripts and their historical and cultural significance has become increasingly challenging. Here, we propose a deep learning approach for verifying whether the scribe who transcribed bamboo slip is the same or not, exploring the handwriting style from an image processing perspective. This approach opens a novel research direction for discriminating against the scribes of bamboo slips. We focus on collecting Warring States bamboo slips housed at Tsinghua University and constructing a self-compiled dataset for scribe verification. The discrimination model resorts to an improved Siamese network framework, utilizing the enhanced lightweight MobileNet_V3+ network for feature extraction and the Squeeze-and-Excitation attention mechanism for weight control. The proposed model achieves rapid and accurate verification among bamboo slip scribes. In addition, the paper elaborates on data augmentation using gray flip to increase the number of tail samples and balance between positive and negative sample pairs. The experimental results on the collected dataset indicate that the scribe verification accuracy of the proposed model reaches 90.2%, with the area under the Receiver Operating Characteristic curve measuring 0.96. We further validate the effectiveness of the proposed model on datasets with unclear scribe attribution. Siamese Network Tsinghua Bamboo Slips Scribe Verification Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2026 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers invited by journal 03 Aug, 2025 Editor assigned by journal 30 Jul, 2025 Submission checks completed at journal 29 Jul, 2025 First submitted to journal 25 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7217961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495762900,"identity":"4f51108b-a7f0-4eed-aa6f-d3f9af5a4dd5","order_by":0,"name":"Haiyang Wang","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Wang","suffix":""},{"id":495762902,"identity":"27aa0eb7-b7af-489a-87c8-200670c301f5","order_by":1,"name":"Mingjun Li","email":"","orcid":"","institution":"Henan 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