appjsonify: An Academic Paper PDF-to-JSON Conversion Toolkit
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Abstract (arXiv)
We present appjsonify, a Python-based PDF-to-JSON conversion toolkit for academic papers. It parses a PDF file using several visual-based document layout analysis models and rule-based text processing approaches. appjsonify is a flexible tool that allows users to easily configure the processing pipeline to handle a specific format of a paper they wish to process. We are publicly releasing appjsonify as an easy-to-install toolkit available via PyPI and GitHub.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 12 references that are in the map. First posted on arXiv 2 Oct 2023.
Builds on
9 of 12 references.
References · 9
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
- 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 553
- 2019PubLayNet: largest dataset ever for document layout analysiscited by 351
- 2023Nougat: Neural Optical Understanding for Academic Documentscited by 245
- 2020DocBank: A Benchmark Dataset for Document Layout Analysiscited by 153
- 2019TableBank: A Benchmark Dataset for Table Detection and Recognitioncited by 102
- 2021VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groupscited by 27
- 2022Scientific Paper Recommendation Systems: a Literature Review of recent Publicationscited by 10
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Citing papers per year: 2023: 1.
1 paper.