Benchmarking Vision-Language Models for French PDF-to-Markdown Conversion
This report evaluates PDF-to-Markdown conversion using recent Vision-Language Models (VLMs) on challenging French documents. Document parsing is a critical step for Retrieval-Augmented Generation (RAG) pipelines, where transcription and layout errors propagate to downstream retrieval and grounding. Existing benchmarks often emphasize English or Chinese and can over-penalize benign formatting and linearization choices (e.g., line breaks, list segmentation, alternative table renderings) that are largely irrelevant for downstream use. We introduce a French-focused benchmark of difficult pages selected via model-disagreement sampling from a corpus of 60{,}000 documents, covering handwritten f...
| Authors | Bruno Rigal, Victor Dupriez, Alexis Mignon et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 February 2026 |
| Last updated at source | 12 February 2026 |
Models and datasets referencing this paper
- pulsia/fr-bench-pdf2mdDataset
- pulsia/fr-bench-pdf2md-resultsDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.