Electrical & Robotic Engineering, University of Shahrood
Abstract
Document images produced by scanner or digital camera, usually suffer from geometric and photometric distortions. Both of them deteriorate the performance of OCR systems. In this paper, we present a novel method to compensate for undesirable geometric distortions aiming to improve OCR results. Our methodology is based on finding text lines by dynamic local connectivity map and then applying a low cost transformation to project curved area to 2-D rectangular area. We evaluate the performance of the proposed methods in combination with three participating methods on the public DFKI-I dataset (CBDAR 2007 dewarping contest), which contains camera-captured document images. Experimental results indicate the effectiveness and superiority of the proposed method.
Shamgholi, M. (2014). Document Image Dewarping Based on Text Line Detection and Surface Modeling (RESEARCH NOTE). International Journal of Engineering, 27(12), 1855-1862.
MLA
Maryam Shamgholi. "Document Image Dewarping Based on Text Line Detection and Surface Modeling (RESEARCH NOTE)". International Journal of Engineering, 27, 12, 2014, 1855-1862.
HARVARD
Shamgholi, M. (2014). 'Document Image Dewarping Based on Text Line Detection and Surface Modeling (RESEARCH NOTE)', International Journal of Engineering, 27(12), pp. 1855-1862.
VANCOUVER
Shamgholi, M. Document Image Dewarping Based on Text Line Detection and Surface Modeling (RESEARCH NOTE). International Journal of Engineering, 2014; 27(12): 1855-1862.
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