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New VLM pipeline extracts structured data from historical auction catalogs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a pipeline called "Lot Machine" to automatically extract structured lot-level metadata from historical auction catalogs. This system utilizes vision-language models (VLMs) and is designed to address the challenges of variable formatting and the need for machine-readable data in provenance research and art market studies. The pipeline was evaluated across different deployment modes, including commercial endpoints, institutional gateways, and local deployments, demonstrating its feasibility and effectiveness for large-scale automated analysis of cultural heritage data. AI

IMPACT Enables large-scale automated analysis of historical auction catalogs, aiding provenance research and art market studies.

RANK_REASON Research paper detailing a new method for data extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New VLM pipeline extracts structured data from historical auction catalogs

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Research paper detailing a new method for data extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mathias Zinnen, Alisha Mund, Sabine Lang, Lukas H\"uttner, Thomas Gorges, Vincent Christlein ·

    Lot Machine: Multimodal Lot Extraction from Auction Catalogs

    arXiv:2608.30510v1 Announce Type: cross Abstract: For provenance research and art market studies, auction catalogs are an essential resource to trace specific objects over time and space. While historical auction catalogs follow established domain conventions, their internal form…

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