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Proceedings Paper

Uyghur language text detection in images
Author(s): Shun Liu; Hongtao Xie; Jian Yin; Yajun Chen
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Paper Abstract

Text detection in images is an important prerequisite for many image content analysis tasks. Actually, nearly all the widely-used methods focus on English and Chinese text detection while some minority language, such as Uyghur language, text detection is paid less attention by researchers. In this paper, we propose a system which detects Uyghur language text in images. First, component candidates are detected by channel-enhanced Maximally Stable Extremal Regions (MSERs) algorithm. Then, most non-text regions are removed by a two-layer filtering mechanism. Next, the rest component regions are connected into short chains, and the short chains are connected into complete chains. Finally, the non-text chains are pruned by a chain elimination filter. To evaluate our algorithm, we generate a new dataset by various Uyghur texts. As a result, experimental comparisons on the proposed dataset prove that our algorithm is effective for detecting Uyghur Language text in complex background images. The F-measure is 83.5%, much better than the state-of-the- art performance of 75.5%.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003345 (29 August 2016); doi: 10.1117/12.2244133
Show Author Affiliations
Shun Liu, Shandong Univ. (China)
Institute of Information Engineering (China)
Hongtao Xie, Institute of Information Engineering (China)
Jian Yin, Shandong Univ. (China)
Yajun Chen, Unit 91917 of PLA (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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