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http://dx.doi.org/10.9728/dcs.2017.18.7.1323

Efficient Object Classification Scheme for Scanned Educational Book Image  

Choi, Young-Ju (Dept. of IT Engineering, Sookmyung Women's University)
Kim, Ji-Hae (Dept. of IT Engineering, Sookmyung Women's University)
Lee, Young-Woon (Dept. of Computer Converged Electronics Engineering, SunMoon University)
Lee, Jong-Hyeok (Dept. of IT Engineering, Sookmyung Women's University)
Hong, Gwang-Soo (Dept. of IT Engineering, Sookmyung Women's University)
Kim, Byung-Gyu (Dept. of IT Engineering, Sookmyung Women's University)
Publication Information
Journal of Digital Contents Society / v.18, no.7, 2017 , pp. 1323-1331 More about this Journal
Abstract
Despite the fact that the copyright has grown into a large-scale business, there are many constant problems especially in image copyright. In this study, we propose an automatic object extraction and classification system for the scanned educational book image by combining document image processing and intelligent information technology like deep learning. First, the proposed technology removes noise component and then performs a visual attention assessment-based region separation. Then we carry out grouping operation based on extracted block areas and categorize each block as a picture or a character area. Finally, the caption area is extracted by searching around the classified picture area. As a result of the performance evaluation, it can be seen an average accuracy of 83% in the extraction of the image and caption area. For only image region detection, up-to 97% of accuracy is verified.
Keywords
Document copyright; Image segmentation; Visual attention information; Deep learning; Caption extraction;
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Times Cited By KSCI : 1  (Citation Analysis)
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