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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Figure text extraction in biomedical literature.

Daehyun Kim¹, Hong Yu

¹Department of Health Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America. kim48@uwm.edu

|January 21, 2011

Summary

This study introduces FigTExT, a novel tool that significantly improves the extraction of text from biomedical figures, enhancing information retrieval for researchers. FigTExT overcomes limitations of standard Optical Character Recognition (OCR) for complex scientific images.

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Area of Science:

Biomedical Informatics
Computational Biology
Scientific Literature Analysis

Background:

Biomedical figures are crucial knowledge repositories in full-text articles.
The increasing volume of publications necessitates efficient computational methods for accessing figure content.
Extracting text directly from figures can aid in mining valuable information.

Purpose of the Study:

To develop and evaluate a specialized tool, FigTExT, for accurate text extraction from biomedical figures.
To enhance the performance of existing Optical Character Recognition (OCR) tools for this specific task.
To improve the discoverability and searchability of information within biomedical figures.

Main Methods:

Evaluated an off-the-shelf Optical Character Recognition (OCR) tool.
Developed FigTExT incorporating image preprocessing, adapted character recognition, and a novel text correction framework using figure-specific lexicons.
Employed image preprocessing to improve text localization and image quality.

Main Results:

FigTExT achieved 84% precision, 98% recall, and 90% F1-score for text localization.
For figure text extraction, FigTExT reported 62.5% precision, 51.0% recall, and 56.2% F1-score.
FigTExT significantly outperformed the baseline OCR tool (25.3% F1-score) and extracted texts not present in captions.

Conclusions:

FigTExT substantially improves text extraction from biomedical figures compared to standard OCR.
The tool's ability to extract embedded figure text enhances the potential for advanced figure search.
FigTExT offers a valuable solution for unlocking information within biomedical visual data.