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A Robust System to Detect and Localize Texts in Natural Scene Images

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3 Author(s)
Yi-Feng Pan ; Nat. Lab. of Pattern Recognition, Chinese Acad. of Sci., Beijing ; Xinwen Hou ; Cheng-Lin Liu

In this paper, we present a robust system to accurately detect and localize texts in natural scene images. For text detection, a region-based method utilizing multiple features and cascade AdaBoost classifier is adopted. For text localization, a window grouping method integrating text line competition analysis is used to generate text lines. Then within each text line, local binarization is used to extract candidate connected components (CCs) and non-text CCs are filtered out by Markov Random Fields (MRF) model, through which text line can be localized accurately. Experiments on the public benchmark ICDAR 2003 Robust Reading and Text Locating Dataset show that our system is comparable to the best existing methods both in accuracy and speed.

Published in:

Document Analysis Systems, 2008. DAS '08. The Eighth IAPR International Workshop on

Date of Conference:

16-19 Sept. 2008

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