# An archaeological Catalog Collection Method Based on Large Vision-Language Models

arXiv: https://arxiv.org/abs/2412.20088 · HTML: https://mlsanity.com/paper/2412.20088/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 28 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents](https://mlsanity.com/paper/2410.10594/) (2024)
- [Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection](https://mlsanity.com/paper/2405.10300/) (2024)
- [Unified Hallucination Detection for Multimodal Large Language Models](https://mlsanity.com/paper/2402.03190/) (2024)
- [LMDX: Language Model-based Document Information Extraction and Localization](https://mlsanity.com/paper/2309.10952/) (2023)
- [Learning to Extract Structured Entities Using Language Models](https://mlsanity.com/paper/2402.04437/) (2024)

## Built on by (most cited first, 1 of 1)

- [PyPottery: an AI-powered end-to-end suite for pottery processing and publication](https://mlsanity.com/paper/2610.02072/) (2026)

## Abstract (arXiv)

Archaeological catalogs, containing key elements such as artifact images, morphological descriptions, and excavation information, are essential for studying artifact evolution and cultural inheritance. These data are widely scattered across publications, requiring automated collection methods. However, existing Large Vision-Language Models (VLMs) and their derivative data collection methods face challenges in accurate image detection and modal matching when processing archaeological catalogs, making automated collection difficult. To address these issues, we propose a novel archaeological catalog collection method based on Large Vision-Language Models that follows an approach comprising three modules: document localization, block comprehension and block matching. Through practical data collection from the Dabagou and Miaozigou pottery catalogs and comparison experiments, we demonstrate the effectiveness of our approach, providing a reliable solution for automated collection of archaeological catalogs.