Xue, Minglong and Shivakumara, Palaiahnakote and Zhang, Chao and Lu, Tong and Pal, Umapada (2019) Curved text detection in blurred/non-blurred video/scene images. Multimedia Tools and Applications, 78 (18). pp. 25629-25653. ISSN 1380-7501, DOI https://doi.org/10.1007/s11042-019-7721-2.
Full text not available from this repository.Abstract
Text detection in video/images is challenging due to the presence of multiple blur caused by defocus and motion. In this paper, we present a new method for detecting texts in blurred/non-blurred images. Unlike the existing methods that use deblurring or classifiers, the proposed method estimates degree of blur in images based on contrast variations in neighbor pixels and a low pass filter, which results in candidate pixels for deblurring. We consider gradient values of each pixel as the weight for the degree of blur. The proposed method then performs K-means clustering on weighted values of candidate pixels to get text candidates irrespective of blur types. Next, Bhattacharyya distance is used to extract symmetry property of texts to remove false text candidates, which provides text components. Further, the proposed method fixes bounding box for each text component based on the nearest neighbor criteria and direction of the text component. Experimental results on defocus, motion, non-blurred images and standard datasets of curved text show that the proposed method outperforms the existing methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
| Item Type: | Article |
|---|---|
| Funders: | Natural Science Foundation of China under Grant No. 61672273 and No. 61272218, Science Foundation for Distinguished Young Scholars of Jiangsu under Grant No. BK20160021, University of Malaya under Grant No: UM.0000520/HRU.BK (BKS003-2018) |
| Uncontrolled Keywords: | Degree of blurriness; Gradient direction; Gradient magnitude; K-means clustering; Motion blurred images |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Nov 2019 02:05 |
| Last Modified: | 28 Nov 2019 02:05 |
| URI: | http://eprints.um.edu.my/id/eprint/23140 |
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