CalliFormer: A Structure-Aware Transformer for Chinese Calligraphy Generation

CalliFormer: A Structure-Aware Transformer for Chinese Calligraphy Generation | 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 CalliFormer: A Structure-Aware Transformer for Chinese Calligraphy Generation Li Zhou, Meng Qi, Yafei Shi, Chunjie Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7582846/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract The digital preservation of Chinese calligraphy, a significant intangible cultural heritage, is impeded by the limitations of traditional generative methods in achieving precise style control and structural fidelity. This paper addresses these shortcomings, which are particularly prevalent in models like CalliGAN, by proposing a novel Transformer-enhanced Generative Adversarial Network (GAN) for calligraphic characters style transfer and generation. Our approach, named Calliformer, tackles three key challenges: (1) We replace RNN-based component encoders, which struggle with capturing global character structures, with a Structure-Aware Transformer encoder. This module models the spatial relationships between components more effectively via a dynamic structural attention bias mechanism. (2) To overcome the limited stylistic diversity of one-hot encodings, we introduce a simple yet effective style encoder that leverages a pre-trained convolutional neural network to extract rich, continuous style embeddings directly from reference images. (3) We introduce CCTS-2025, a publicly available calligraphy dataset annotated with explicit Chinese character structural relationships, to facilitate research in this domain. Experimental results demonstrate that our method achieves a 33.8% reduction in Mean Squared Error (MSE) (from 19.49 to 12.91) and an increase of 0.0965 in Structural Similarity Index (SSIM) compared to the state-of-the-art baseline. Furthermore, a human evaluation study reveals that 91.2% of the generated calligraphy is recognized as authentic and stylistically superior by human participants. This research offers a new paradigm for cultural heritage digitization, with significant application potential in calligraphy education and artifact restoration. Physical sciences/Engineering Physical sciences/Mathematics and computing Chinese calligraphy generation style encoder generative adversarial networks (GANs) Transformer cultural heritage digitization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 25 Sep, 2025 Editor invited by journal 25 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 24 Sep, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7582846","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525969401,"identity":"720893ba-d128-4c93-b31e-b5b990306b21","order_by":0,"name":"Li Zhou","email":"","orcid":"","institution":"Luoyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhou","suffix":""},{"id":525969402,"identity":"8f569f11-2986-423c-9821-9acfc8d9544a","order_by":1,"name":"Meng 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This paper addresses these shortcomings, which are particularly prevalent in models like CalliGAN, by proposing a novel Transformer-enhanced Generative Adversarial Network (GAN) for calligraphic characters style transfer and generation. Our approach, named Calliformer, tackles three key challenges: (1) We replace RNN-based component encoders, which struggle with capturing global character structures, with a Structure-Aware Transformer encoder. This module models the spatial relationships between components more effectively via a dynamic structural attention bias mechanism. (2) To overcome the limited stylistic diversity of one-hot encodings, we introduce a simple yet effective style encoder that leverages a pre-trained convolutional neural network to extract rich, continuous style embeddings directly from reference images. 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