A super resolution framework for low resolution document image OCR

Di Ma; Gady Agam

doi:10.1117/12.2008354

4 February 2013 A super resolution framework for low resolution document image OCR

Di Ma, Gady Agam

Proceedings Volume 8658, Document Recognition and Retrieval XX; 86580P (2013) https://doi.org/10.1117/12.2008354
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

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.

Citation Download Citation

Di Ma and Gady Agam "A super resolution framework for low resolution document image OCR", Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580P (4 February 2013); https://doi.org/10.1117/12.2008354

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