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Open Access Highly Accessed Software review

Layout-aware text extraction from full-text PDF of scientific articles

Cartic Ramakrishnan1*, Abhishek Patnia2, Eduard Hovy1 and Gully APC Burns1

Author Affiliations

1 Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA, 90292-6695, USA

2 Computer Science Department, University of Southern California, 941 Bloom Walker, Los Angeles, CA, 90089-0781, USA

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Source Code for Biology and Medicine 2012, 7:7  doi:10.1186/1751-0473-7-7

Published: 28 May 2012



The Portable Document Format (PDF) is the most commonly used file format for online scientific publications. The absence of effective means to extract text from these PDF files in a layout-aware manner presents a significant challenge for developers of biomedical text mining or biocuration informatics systems that use published literature as an information source. In this paper we introduce the ‘Layout-Aware PDF Text Extraction’ (LA-PDFText) system to facilitate accurate extraction of text from PDF files of research articles for use in text mining applications.


Our paper describes the construction and performance of an open source system that extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles and is meant as a baseline for further experiments into more advanced extraction methods that handle multi-modal content, such as images and graphs. The system works in a three-stage process: (1) Detecting contiguous text blocks using spatial layout processing to locate and identify blocks of contiguous text, (2) Classifying text blocks into rhetorical categories using a rule-based method and (3) Stitching classified text blocks together in the correct order resulting in the extraction of text from section-wise grouped blocks. We show that our system can identify text blocks and classify them into rhetorical categories with Precision1 = 0.96% Recall = 0.89% and F1 = 0.91%. We also present an evaluation of the accuracy of the block detection algorithm used in step 2. Additionally, we have compared the accuracy of the text extracted by LA-PDFText to the text from the Open Access subset of PubMed Central. We then compared this accuracy with that of the text extracted by the PDF2Text system, 2commonly used to extract text from PDF. Finally, we discuss preliminary error analysis for our system and identify further areas of improvement.


LA-PDFText is an open-source tool for accurately extracting text from full-text scientific articles. The release of the system is available at webcite.