Automated Text Detection and Recognition in Annotated Biomedical Publication Images

Soumya De, R. Joe Stanley, Beibei Cheng, Sameer Antani, Rodney Long and George Thoma
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Soumya De: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA
R. Joe Stanley: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA
Beibei Cheng: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA
Sameer Antani: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
Rodney Long: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
George Thoma: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2014, vol. 9, issue 2, 34-63

Abstract: Images in biomedical publications often convey important information related to an article's content. When referenced properly, these images aid in clinical decision support. Annotations such as text labels and symbols, as provided by medical experts, are used to highlight regions of interest within the images. These annotations, if extracted automatically, could be used in conjunction with either the image caption text or the image citations (mentions) in the articles to improve biomedical information retrieval. In the current study, automatic detection and recognition of text labels in biomedical publication images was investigated. This paper presents both image analysis and feature-based approaches to extract and recognize specific regions of interest (text labels) within images in biomedical publications. Experiments were performed on 6515 characters extracted from text labels present in 200 biomedical publication images. These images are part of the data set from ImageCLEF 2010. Automated character recognition experiments were conducted using geometry-, region-, exemplar-, and profile-based correlation features and Fourier descriptors extracted from the characters. Correct recognition as high as 92.67% was obtained with a support vector machine classifier, compared to a 75.90% correct recognition rate with a benchmark Optical Character Recognition technique.

Date: 2014
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