Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network-based text location in color images
Pattern Recognition Letters
Recognizing Characters in Scene Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating text in complex color images
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Robust Extraction of Text in Video
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Integrating image data into biomedical text categorization
Bioinformatics
Natural language processing and visualization in the molecular imaging domain
Journal of Biomedical Informatics
Text detection, localization, and tracking in compressed video
Image Communication
International Journal on Document Analysis and Recognition
Text Particles Multi-band Fusion for Robust Text Detection
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Bioinformatics
Page segmentation using texture analysis
Pattern Recognition
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
A comprehensive method for multilingual video text detection, localization, and extraction
IEEE Transactions on Circuits and Systems for Video Technology
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There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use.