4 February 2013 A super resolution framework for low resolution document image OCR
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Abstract
Optical character recognition is widely used for converting document images into digital media. Existing OCR algorithms and tools produce good results from high resolution, good quality, document images. In this paper, we propose a machine learning based super resolution framework for low resolution document image OCR. Two main techniques are used in our proposed approach: a document page segmentation algorithm and a modified K-means clustering algorithm. Using this approach, by exploiting coherence in the document, we reconstruct from a low resolution document image a better resolution image and improve OCR results. Experimental results show substantial gain in low resolution documents such as the ones captured from video.
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Di Ma, Di Ma, Gady Agam, Gady Agam, "A super resolution framework for low resolution document image OCR ", Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580P (4 February 2013); doi: 10.1117/12.2008354; https://doi.org/10.1117/12.2008354
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