[Submitted on 30 Jun 2026]
Title:Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention
View a PDF of the paper titled Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention, by Chaowen Yan and 4 other authors
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Abstract:Oracle Bone Inscriptions (OBIs) recognition plays a crucial role in understanding ancient Chinese culture. However, accurately recognizing OBIs remains highly challenging due to their complex, irregular, and often degraded shapes. Traditional methods rely on expert knowledge and manual analysis, which are time-consuming and error-prone. Although deep learning has greatly advanced general image recognition, existing methods struggle to capture the fine-grained details and subtle variations inherent in OBIs, resulting in limited performance. Even most recent and effective layer attention techniques are designed to capture fine-grained dependencies through enhanced inter-layer interactions, yet they still exhibit only marginal improvements in OBIs recognition. To address these limitations, we propose Multi-Scale Layer Attention (MSLA), a novel paradigm that explicitly models both multi-scale and cross-layer feature interactions. By enriching the representation with fine-grained details across multiple spatial scales, MSLA enables more accurate and robust OBIs recognition. Extensive experiments on large-scale OBIs datasets demonstrate that MSLA consistently outperforms existing attention mechanisms while maintaining computational efficiency.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.00057 [cs.CV]
(or arXiv:2607.00057v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.00057
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tao He [view email] [v1] Tue, 30 Jun 2026 07:48:05 UTC (10,455 KB)
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