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Detecting Figure-Panel Labels In Medical Journal Articles Using MRF.
We present a method for figure-panel (subfigure) label detection and recognition in multi-panel figures extracted from biomedical articles. Figures in biomedical articles often comprise several subfigures that are identified by superimposed panel labels (‘A’, ‘B’, …) which are referenced in the figure caption and discussion in the article body. Splitting such multi-panel figures into individual subfigures is a necessary step for improved multimodal biomedical information retrieval. Prior to feature extraction for indexing and retrieval of biomedical figures it is necessary to classify image content in each subfigure by its modality (X-ray, MRI, CT, etc.) and other relevant criteria. Subfigure labels are valuable in associating individual panels with relevant text in captions and discussion. We propose a 4-step panel label detection method based on Markov Random Field (MRF). Experiments on 515 multi-panel figures and analysis of the results show promising results. We present the successes and
identify critical challenges.
Keywords- image-text detection; Markov Random Field; belief propagation; OCR; Neural network; image binarization; CBIR; image classification