PubMed-OCR: PMC Open Access OCR Annotations
Hunter Heidenreich, Yosheb Getachew, Olivia Dinica, Ben Elliott
TL;DR
PubMed-OCR is a large, annotated corpus of scientific articles from PubMed Central, designed to support layout-aware OCR modeling and evaluation, with detailed annotations at multiple levels.
Contribution
The paper introduces a comprehensive OCR-centric dataset with detailed annotations for scientific articles, enabling advanced layout-aware OCR research and evaluation.
Findings
Corpus includes 209.5K articles and 1.3B words.
Annotations support layout-aware modeling and QA tasks.
Analysis reveals coverage and limitations of the dataset.
Abstract
PubMed-OCR is an OCR-centric corpus of scientific articles derived from PubMed Central Open Access PDFs. Each page image is annotated with Google Cloud Vision and released in a compact JSON schema with word-, line-, and paragraph-level bounding boxes. The corpus spans 209.5K articles (1.5M pages; ~1.3B words) and supports layout-aware modeling, coordinate-grounded QA, and evaluation of OCR-dependent pipelines. We analyze corpus characteristics (e.g., journal coverage and detected layout features) and discuss limitations, including reliance on a single OCR engine and heuristic line reconstruction. We release the data and schema to facilitate downstream research and invite extensions.
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Handwritten Text Recognition Techniques · Cell Image Analysis Techniques