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

arXiv: https://arxiv.org/abs/2609.26381 · HTML: https://mlsanity.com/paper/2609.26381/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations](https://mlsanity.com/paper/2412.07626/) (2024)
- [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://mlsanity.com/paper/2206.01062/) (2022)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing](https://mlsanity.com/paper/2509.22186/) (2025)
- [Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion](https://mlsanity.com/paper/2501.17887/) (2025)
- [olmOCR 2: Unit Test Rewards for Document OCR](https://mlsanity.com/paper/2510.19817/) (2025)
- [GriTS: Grid table similarity metric for table structure recognition](https://mlsanity.com/paper/2203.12555/) (2022)
- [VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups](https://mlsanity.com/paper/2106.00676/) (2021)
- [PP-DocLayout: A Unified Document Layout Detection Model to Accelerate Large-Scale Data Construction](https://mlsanity.com/paper/2503.17213/) (2025)
- [Does Bigger Mean Better? Comparitive Analysis of CNNs and Biomedical Vision Language Modles in Medical Diagnosis](https://mlsanity.com/paper/2510.00411/) (2025)
- [Automatic extraction of materials and properties from superconductors scientific literature](https://mlsanity.com/paper/2210.15600/) (2022)
- [HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding](https://mlsanity.com/paper/2602.12957/) (2026)
- [Efficient Document Parsing via Parallel Token Prediction](https://mlsanity.com/paper/2603.15206/) (2026)
- [Monitoring Open Access at a national level: French case study](https://mlsanity.com/paper/2104.06844/) (2021)
- [SciLaD: A Large-Scale, Transparent, Reproducible Dataset for Natural Scientific Language Processing](https://mlsanity.com/paper/2512.11192/) (2025)

## Abstract (arXiv)

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, dz=1.08), and caption-linked figure recovery improves on both corpora. On the external Table-BRGM benchmark, table detection recovers F1 0.16 → 0.94 and table structure follows (GriTS-Top 0.27 → 0.78, below the strongest GPU system). On body text, against four vision-based systems (Docling, MinerU, olmOCR, dots.ocr), 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× less than the cheapest GPU system (Docling) and 10--14× less than generative parsers.