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Proceedings Paper

A novel Iterative algorithm to text segmentation for web born-digital images
Author(s): Zhigang Xu; Yuesheng Zhu; Ziqiang Sun; Zhen Liu
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Paper Abstract

Since web born-digital images have low resolution and dense text atoms, text region over-merging and miss detection are still two open issues to be addressed. In this paper a novel iterative algorithm is proposed to locate and segment text regions. In each iteration, the candidate text regions are generated by detecting Maximally Stable Extremal Region (MSER) with diminishing thresholds, and categorized into different groups based on a new similarity graph, and the texted region groups are identified by applying several features and rules. With our proposed overlap checking method the final well-segmented text regions are selected from these groups in all iterations. Experiments have been carried out on the web born-digital image datasets used for robust reading competition in ICDAR 2011 and 2013, and the results demonstrate that our proposed scheme can significantly reduce both the number of over-merge regions and the lost rate of target atoms, and the overall performance outperforms the best compared with the methods shown in the two competitions in term of recall rate and f-score at the cost of slightly higher computational complexity.

Paper Details

Date Published: 6 July 2015
PDF: 6 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963103 (6 July 2015); doi: 10.1117/12.2197039
Show Author Affiliations
Zhigang Xu, Peking Univ. (China)
Yuesheng Zhu, Peking Univ. (China)
Ziqiang Sun, Peking Univ. (China)
Zhen Liu, Peking Univ. (China)

Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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