# OCR++: A Robust Framework For Information Extraction from Scholarly Articles

arXiv: https://arxiv.org/abs/1609.06423 · HTML: https://mlsanity.com/paper/1609.06423/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling](https://mlsanity.com/paper/2112.12940/) (2021)
- [MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles](https://mlsanity.com/paper/2212.05429/) (2022)
- [Finding Pragmatic Differences Between Disciplines](https://mlsanity.com/paper/2310.00204/) (2023)
- [Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline](https://mlsanity.com/paper/2505.11177/) (2025)
- [Structured Layout Priors for Robust Out-of-Distribution Visual Document Understanding](https://mlsanity.com/paper/2605.19866/) (2026)

## Abstract (arXiv)

This paper proposes OCR++, an open-source framework designed for a variety of information extraction tasks from scholarly articles including metadata (title, author names, affiliation and e-mail), structure (section headings and body text, table and figure headings, URLs and footnotes) and bibliography (citation instances and references). We analyze a diverse set of scientific articles written in English language to understand generic writing patterns and formulate rules to develop this hybrid framework. Extensive evaluations show that the proposed framework outperforms the existing state-of-the-art tools with huge margin in structural information extraction along with improved performance in metadata and bibliography extraction tasks, both in terms of accuracy (around 50% improvement) and processing time (around 52% improvement). A user experience study conducted with the help of 30 researchers reveals that the researchers found this system to be very helpful. As an additional objective, we discuss two novel use cases including automatically extracting links to public datasets from the proceedings, which would further accelerate the advancement in digital libraries. The result of the framework can be exported as a whole into structured TEI-encoded documents. Our framework is accessible online at http://cnergres.iitkgp.ac.in/OCR++/home/.