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Accurate and robust text detection: a step-in for text retrieval in natural scene images

Published: 28 July 2013 Publication History

Abstract

We propose and implement a robust text detection system, which is a prominent step-in for text retrieval in natural scene images or videos. Our system includes several key components: (1) A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions as character candidates using the strategy of minimizing regularized variations. (2) Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and threshold of clustering are learned automatically by a novel self-training distance metric learning algorithm. (3) The posterior probabilities of text candidates corresponding to non-text are estimated with an character classifier; text candidates with high probabilities are then eliminated and finally texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition dataset and a publicly available multilingual dataset; the f measures are over 76% and 74% which are significantly better than the state-of-the-art performances of 71% and 65%, respectively.

References

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J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. In Proceedings of BMVC, volume 1, pages 384--393, 2002.
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L. Neumann and J. Matas. A method for text localization and recognition in real-world images. In Proceedings of the 10th ACCV, volume 3, pages 770--783, 2011.
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L. Neumann and J. Matas. Real-time scene text localization and recognition. In Proceedings of IEEE CVPR, pages 3538--3545, 2012.
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Y.-F. Pan, X. Hou, and C.-L. Liu. A hybrid approach to detect and localize texts in natural scene images. IEEE Trans. Image Processing, 20(3):800--813, 2011.
[5]
A. Shahab, F. Shafait, and A. Dengel. ICDAR 2011 robust reading competition challenge 2: Reading text in scene images. In Proceedings of ICDAR, pages 1491--1496, 2011.
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C. Shi, C. Wang, B. Xiao, Y. Zhang, and S. Gao. Scene text detection using graph model built upon maximally stable extremal regions. Pattern Recognition Letters, 34(2):107--116, 2013.
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X. Yin, X.-C. Yin, H.-W. Hao, and K. Iqbal. Effective text localization in natural scene images with MSER, geometry-based grouping and Adaboost. In Proceedings of ICPR, pages 725--728, 2012.
[8]
X.-C. Yin, X. Yin, K. Huang, and H.-W. Hao. Robust text detection in natural scene images. CoRR abs/1301.2628, 2013.

Cited By

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  • (2021)Robust detection of video text using an efficient hybrid method via key frame extraction and text localizationMultimedia Tools and Applications10.1007/s11042-020-10113-280:6(9671-9686)Online publication date: 1-Mar-2021
  • (2021)Scene Text Recognition in the Wild with Motion Deblurring Using Deep NetworksComputer Vision and Image Processing10.1007/978-981-16-1103-2_9(93-103)Online publication date: 26-Mar-2021
  • (2019)A Natural Scene Text Extraction Approach Based on Generative Adversarial LearningNeural Information Processing10.1007/978-3-030-36708-4_6(65-73)Online publication date: 12-Dec-2019
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  1. Accurate and robust text detection: a step-in for text retrieval in natural scene images

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    cover image ACM Conferences
    SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
    July 2013
    1188 pages
    ISBN:9781450320344
    DOI:10.1145/2484028
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 28 July 2013

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    Author Tags

    1. distance metric learning
    2. maximally stable extremal regions
    3. scene text detection
    4. single-link clustering

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    SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    View all
    • (2021)Robust detection of video text using an efficient hybrid method via key frame extraction and text localizationMultimedia Tools and Applications10.1007/s11042-020-10113-280:6(9671-9686)Online publication date: 1-Mar-2021
    • (2021)Scene Text Recognition in the Wild with Motion Deblurring Using Deep NetworksComputer Vision and Image Processing10.1007/978-981-16-1103-2_9(93-103)Online publication date: 26-Mar-2021
    • (2019)A Natural Scene Text Extraction Approach Based on Generative Adversarial LearningNeural Information Processing10.1007/978-3-030-36708-4_6(65-73)Online publication date: 12-Dec-2019
    • (2017)Current trends in text_spotting2017 International Conference on Wireless Networks and Mobile Communications (WINCOM)10.1109/WINCOM.2017.8238215(1-6)Online publication date: Nov-2017
    • (2017)Message From the Outgoing Editor-in-ChiefIEEE Transactions on Multimedia10.1109/TMM.2017.265785819:3(445-445)Online publication date: 1-Mar-2017
    • (2017)A Convolutional Neural Network-Based Chinese Text Detection Algorithm via Text Structure ModelingIEEE Transactions on Multimedia10.1109/TMM.2016.262525919:3(506-518)Online publication date: 1-Mar-2017
    • (2017)Tracking Based Multi-Orientation Scene Text DetectionIEEE Transactions on Image Processing10.1109/TIP.2017.269510426:7(3235-3248)Online publication date: 1-Jul-2017
    • (2017)Smart IDReader: Document Recognition in Video Stream2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2017.347(39-44)Online publication date: Nov-2017
    • (2017)Scene-Text-Detection Method Robust Against Orientation and Discontiguous Components of Characters2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW.2017.130(941-949)Online publication date: Jul-2017
    • (2017)Fuzzy based edge enhanced text detection algorithm using MSERCluster Computing10.1007/s10586-017-1448-522:S5(11681-11687)Online publication date: 9-Dec-2017
    • Show More Cited By

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