# appjsonify: An Academic Paper PDF-to-JSON Conversion Toolkit

arXiv: https://arxiv.org/abs/2310.01206 · HTML: https://mlsanity.com/paper/2310.01206/

## Key facts

- Cited by 1 papers 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 (most cited first, 9 of 9)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [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)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups](https://mlsanity.com/paper/2106.00676/) (2021)
- [Scientific Paper Recommendation Systems: a Literature Review of recent Publications](https://mlsanity.com/paper/2201.00682/) (2022)

## Built on by (most cited first, 1 of 1)

- [Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2310.10520/) (2023)

## 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.