PAWLS: PDF Annotation With Labels and Structure
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Abstract (arXiv)
Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to use the information contained within PDF documents for training models or data analysis, because annotating these documents is difficult. In this paper, we present PDF Annotation with Labels and Structure (PAWLS), a new annotation tool designed specifically for the PDF document format. PAWLS is particularly suited for mixed-mode annotation and scenarios in which annotators require extended context to annotate accurately. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models. A read-only PAWLS server is available at https://pawls.apps.allenai.org/ and the source code is available at https://github.com/allenai/pawls.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 25 Jan 2021.
Builds on
14 of 27 references.
References · 14
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 1.
4 papers.
- 2021VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groupscited by 27
- 2024BuDDIE: A Business Document Dataset for Multi-task Information Extractioncited by 8
- 2025DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiralcited by 2
- 2024KnowledgeHub: An end-to-end Tool for Assisted Scientific Discoverycited by 1
- 2025DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral6 May 2025
- 2024KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery16 May 2024
- 2024BuDDIE: A Business Document Dataset for Multi-task Information Extraction5 Apr 2024
- 2021VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups1 Jun 2021