# Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark

arXiv: https://arxiv.org/abs/2504.09555 · HTML: https://mlsanity.com/paper/2504.09555/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 55 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Invertible Denoising Network: A Light Solution for Real Noise Removal](https://mlsanity.com/paper/2104.10546/) (2021)
- [KBNet: Kernel Basis Network for Image Restoration](https://mlsanity.com/paper/2303.02881/) (2023)
- [OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?](https://mlsanity.com/paper/2412.01175/) (2024)
- [Deciphering Oracle Bone Language with Diffusion Models](https://mlsanity.com/paper/2406.00684/) (2024)
- [AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation](https://mlsanity.com/paper/2303.07012/) (2023)
- [CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising](https://mlsanity.com/paper/2207.07798/) (2022)
- [An open dataset for the evolution of oracle bone characters: EVOBC](https://mlsanity.com/paper/2401.12467/) (2024)
- [CascadedGaze: Efficiency in Global Context Extraction for Image Restoration](https://mlsanity.com/paper/2401.15235/) (2024)
- [Unsupervised Structure-Texture Separation Network for Oracle Character Recognition](https://mlsanity.com/paper/2205.06549/) (2022)
- [RZCR: Zero-shot Character Recognition via Radical-based Reasoning](https://mlsanity.com/paper/2207.05842/) (2022)
- [RCRN: Real-world Character Image Restoration Network via Skeleton Extraction](https://mlsanity.com/paper/2207.07795/) (2022)
- [Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts](https://mlsanity.com/paper/2502.14865/) (2025)
- [Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations](https://mlsanity.com/paper/2308.00655/) (2023)
- [OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion](https://mlsanity.com/paper/2411.17837/) (2024)
- [Oracle Character Recognition using Unsupervised Discriminative Consistency Network](https://mlsanity.com/paper/2312.06075/) (2023)
- [A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond](https://mlsanity.com/paper/2411.11354/) (2024)
- [Reasoning Over the Glyphs: Evaluation of LLM's Decipherment of Rare Scripts](https://mlsanity.com/paper/2501.17785/) (2025)

## Built on by (most cited first, 5 of 5)

- [PictOBI-20k: Unveiling Large Multimodal Models in Visual Decipherment for Pictographic Oracle Bone Characters](https://mlsanity.com/paper/2509.05773/) (2025)
- [Interpretable Oracle Bone Script Decipherment through Radical and Pictographic Analysis with LVLMs](https://mlsanity.com/paper/2508.10113/) (2025)
- [Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning](https://mlsanity.com/paper/2604.11299/) (2026)
- [Towards Computational Chinese Paleography](https://mlsanity.com/paper/2601.06753/) (2026)
- [Beyond Single Character: Evaluating MLLMs for Sentence-Level Oracle Bone Inscription Understanding](https://mlsanity.com/paper/2606.31169/) (2026)

## Abstract (arXiv)

The oracle bone inscription (OBI) recognition plays a significant role in understanding the history and culture of ancient China. However, the existing OBI datasets suffer from a long-tail distribution problem, leading to biased performance of OBI recognition models across majority and minority classes. With recent advancements in generative models, OBI synthesis-based data augmentation has become a promising avenue to expand the sample size of minority classes. Unfortunately, current OBI datasets lack large-scale structure-aligned image pairs for generative model training. To address these problems, we first present the Oracle-P15K, a structure-aligned OBI dataset for OBI generation and denoising, consisting of 14,542 images infused with domain knowledge from OBI experts. Second, we propose a diffusion model-based pseudo OBI generator, called OBIDiff, to achieve realistic and controllable OBI generation. Given a clean glyph image and a target rubbing-style image, it can effectively transfer the noise style of the original rubbing to the glyph image. Extensive experiments on OBI downstream tasks and user preference studies show the effectiveness of the proposed Oracle-P15K dataset and demonstrate that OBIDiff can accurately preserve inherent glyph structures while transferring authentic rubbing styles effectively.