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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.<\/p>","DOI":"10.4018\/ijhisi.2014040103","type":"journal-article","created":{"date-parts":[[2014,10,6]],"date-time":"2014-10-06T12:07:05Z","timestamp":1412597225000},"page":"34-63","source":"Crossref","is-referenced-by-count":1,"title":["Automated Text Detection and Recognition in Annotated Biomedical Publication Images"],"prefix":"10.4018","volume":"9","author":[{"given":"Soumya","family":"De","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. 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