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Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion — arXiv:2609.26381

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.26381
License (as archived): CC0-1.0
Archived: 2026-10-08T14:36:46.144Z
SHA-256 of the archived text: 9c0ac01dd808d8539627fadecd61ede1624f2fa4e37f6422b81b6e443b23c87e
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How to cite

Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion — arXiv:2609.26381 — arXiv (en), https://arxiv.org/abs/2609.26381 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_320de07cea0d (Metraipolis archive dc_320de07cea0d, sha256 9c0ac01dd808d8539627fadecd61ede1624f2fa4e37f6422b81b6e443b23c87e)

BibTeX

@misc{dc320de07cea0d,
  title = {Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion — arXiv:2609.26381},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.26381},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_320de07cea0d; sha256 9c0ac01dd808d8539627fadecd61ede1624f2fa4e37f6422b81b6e443b23c87e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.26381. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Transforming scholarly PDFs into machine-readable fulltext remains a bottleneck for large-scale information systems. Recent vision-based parsers improve accuracy, but need GPUs and may introduce noise into the extracted text. GROBID, a modular font-stream parser running on CPU, is the de-facto standard for structuring scientific articles and underpins several of the largest open scholarly corpora. We pair it with a lightweight CPU detector localising figure, table, and paratext (header, footer, page number) regions, encoded as typed-area masks whose tokens are routed to GROBID's specialised models or discarded. On two PMC corpora, Bioinformatics (1,926 articles) and Materials Science (2,595), scored against JATS with a section-aware structural protocol, our extension improves over plain GROBID on most metrics (NS $+0.025$/$+0.013$; $+0.086$ paragraph recall on Materials Science, $d_z{=}1.08$), and caption-linked figure recovery improves on both corpora. On the external Table-BRGM benchmark, table detection recovers F1 $0.16 \to 0.94$ and table structure follows (GriTS-Top $0.27 \to 0.78$, below the strongest GPU system). On body text, against four vision-based systems (Docling, MinerU, olmOCR, this http URL ), it has the best paragraph precision on both corpora, the best section detection on Materials Science, and a character error rate within 0.004 of the best GPU parser. End-to-end on CPU, it costs $2.7$--$3.2\times$ less than the cheapest GPU system (Docling) and $10$--$14\times$ less than generative parsers.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-23 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Digital Libraries (cs.DL).
Pagina del lavoro: https://arxiv.org/abs/2609.26381 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T13:46:15.745Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).