2013
CVPR
CVPR 2013
A Non-parametric Framework for Document Bleed-through Removal
Abstract
This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subsequent image labelling. Finally restoration of the degraded regions is performed using exemplar-based image inpainting. The proposed method is evaluated visually and numerically on a freely available database of 25 scanned manuscript image pairs with ground truth, and is shown to outperform recent non-blind bleed-through removal techniques.
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Conference Pioneer
— CVPR 2013
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Interdisciplinary Bridge
— Computer Science and Computer Vision and Machine Learning
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Trend Setter
— Data Augmentation
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Keyword Pioneer
— image preprocessing
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Hot Topic Early Bird
— document analysis
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio