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Color text extraction with selective metric-based clustering

Authors:

Céline Mancas-Thillou,

Bernard GosselinAuthors Info & Claims

Computer Vision and Image Understanding, Volume 107, Issue 1-2

Pages 97 - 107

https://doi.org/10.1016/j.cviu.2006.11.010

Published: 01 July 2007 Publication History

Abstract

Natural scene images usually contain varying colors which make segmentation more difficult. Without any a priori knowledge of degradations and based on physical light reflectance, we propose a selective metric-based clustering to extract textual information in real-world images. The proposed method uses several metrics to merge similar color together for an efficient text-driven segmentation in the RGB color space. However, color information by itself is not sufficient to solve all natural scene issues; hence we complement it with intensity and spatial information obtained using Log-Gabor filters, thus enabling the processing of character segmentation into individual components to increase final recognition rates. Hence, our selective metric-based clustering is integrated into a dynamic method suitable for text extraction and character segmentation. Quantitative results on a public database are presented to assess the efficiency and the complementarity of metrics, together with the importance of a dynamic system for natural scene text extraction. Finally running time is detailed to show the usability of our method.

References

[1]

B. Gatos, I. Pratikakis, K. Kepene, S.J. Perantonis, Text detection in indoor/outdoor scene images, in: Proc. First Workshop of Camera-based Document Analysis and Recognition, 2005, pp. 127-132.

[2]

Jung, K., Kim, K.I. and Jain, A.K., Text information extraction in images and video: a survey. Pattern Recognit. v37. 977-997.

[3]

Hase, H., Shinokawa, T., Yoneda, M. and Suen, C.Y., Character string extraction from color documents. Pattern Recognit. v34. 1349-1365.

[4]

D. Karatzas, A. Antonacopoulos, Text extraction from web images based on a split-and-merge segmentation method using colour perception, in: Proc. Int. Conf. Pattern Recognition, 2004, pp. 634-637.

[5]

Y. Du, C. Chang, P. Thouin, Unsupervised approach to color video thresholding, in: Proc. SPIE Optical Imaging, vol. 43, n.2, 2004, pp. 282-289.

[6]

Y. Liu, S. Goto, T. Ikenaga, A robust algorithm for text detection in color images, in: Proc. Int. Conf. Document Analysis and Recognition, 2005, pp. 399-403.

Digital Library

[7]

J. Gao, J. Yang, Y. Zhang, A. Waibel, Text detection and translation from natural scenes, Carnegie Mellon University Tech. Report CMU-CS-01-139, 2001.

[8]

Crandall, D., Antani, S. and Kasturi, R., Extraction of special effects caption text events from digital video. Int. J. Doc. Anal. Recognit. v5. 138-157.

[9]

B. Wang, X.-F. Li, F. Liu, F.-Q. Hu, Color text image binarization based on binary texture analysis, in: Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2004, pp. 585-588.

[10]

C. Garcia, X. Apostolidis, Text detection and segmentation in complex color images, in: Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2000, pp. 2326-2330.

[11]

C. Thillou, B. Gosselin, Color binarization for complex camera-based images, in: Proc. Electronic Imaging Conf. Int. Soc. Opt. Imaging, 2005, pp. 301-308.

[12]

Lucas, S., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R., Ashida, K., Nagai, H., Okamoto, M., Yamamoto, H., Miyao, H., Zu, Y., Ou, W., Wolf, C., Jolion, J.-M., Todoran, L., Worring, M. and Lin, X., ICDAR 2003 robust reading competitions: entries, results and future directions. Int. J. Doc. Anal. Recognit. v7. 105-122.

Digital Library

[13]

Sharma, G., Digital Color Imaging Handbook. 2003. CRC Press LLC, Boca Raton.

[14]

Shafer, S.A., Using Color to Separate Reflection Components, TR-136. 1984. Computer Sciences Dept., University of Rochester, New York.

[15]

Gevers, T., Color in Image Databases, Isis Report. 2000. University of Amsterdam, The Netherlands.

[16]

B.T. Phong, Illumination for computer generated pictures, Communications of the ACM, vol. 18, n.6, 1975, pp. 311-317.

Digital Library

[17]

W. Skarbek, A. Koschan, Colour image segmentation - a survey-, Technischer Bericht 94-32, TU Berlin, 1994.

[18]

C. Mancas-Thillou, B. Gosselin, Color text extraction from camera-based images-the impact of the choice of the clustering distance-, in: Proc. Int. Conf. Document Analysis Recognition, 2005, pp. 312-316.

[19]

Lukac, R. and Plataniotis, K.N., A taxonomy of color image filtering and enhancement solutions. Adv. Imaging Electron Phys. v140. 187-264.

[20]

Wesolkowski, S. and Jernigan, E., Color edge detection in rgb using jointly Euclidean distance and vector angle. 1999. Vision Interface, Canada.

[21]

S. Wesolkowski, Shading and highlight invariant color segmentation, EAC Tech. Research Report, University of Waterloo, Canada, 2000.

[22]

Hild, M., Color similarity measures for efficient color classification. J. Imaging Sci. Tech. 529-547.

[23]

Lukac, R., Smolka, B., Martin, K., Plataniotis, K.N. and Venetsanopoulos, A.N., Vector filtering for color imaging. IEEE Signal Process. Mag. v22. 74-86.

[24]

Lukac, R., Plataniotis, K.N., Smolka, B. and Venetsanopoulos, A.N., Generalized selection weighted vector filters. Eurasip J. Appl. Signal Process. v12. 1870-1885.

[25]

C. Mancas-Thillou, M. Mirmehdi, Super-resolution text using the Teager filter, in: Proc. Camera-based Document Analysis and Recognition, 2005, pp. 10-16.

[26]

S. Lucas, C. Jaimez Gonzales, Web-based deployment of text locating algorithms, in: Proc. Camera-based Document Analysis and Recognition, 2005, pp. 101-107.

[27]

Otsu, N., A thresholding selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. v8. 62-66.

[28]

Field, D.J., Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. 2379-2394.

[29]

Thillou, C., Ferreira, S. and Gosselin, B., An embedded application for degraded text recognition. Eurasip J. Appl. Signal Process. Spec. Issue Adv. Intell. Vis. Syst. Methods Appl. v13. 2127-2135.

[30]

Robust reading competition of ICDAR, <http://algoval.essex.ac.uk/icdar>, 2003.

[31]

V.I. Levenshtein, Binary codes capable of correcting deletions, insertions and reversals, Soviet Physics Doklady, vol. 10, n.8, 1966, pp. 707-710.

[32]

Kaufman, L. and Rousseeuw, P.J., Finding groups in data: an introduction to cluster analysis. 1990. Wiley Editions, New York.

Cited By

Li MBai MLv Y(2022)Text segmentation by integrating hybrid strategy and non-text filteringMultimedia Tools and Applications10.1007/s11042-022-13029-181:30(44505-44522)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11042-022-13029-1
Zhu AUchida S(2019)Scene word recognition from pieces to wholeFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6420-213:2(292-301)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11704-017-6420-2
Yan CXie HChen JZha ZHao XZhang YDai Q(2018)A Fast Uyghur Text Detector for Complex Background ImagesIEEE Transactions on Multimedia10.1109/TMM.2018.283832020:12(3389-3398)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1109/TMM.2018.2838320
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Index Terms

Color text extraction with selective metric-based clustering
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
    2. Machine learning algorithms
      1. Feature selection

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Information

Published In

cover image Computer Vision and Image Understanding

Computer Vision and Image Understanding Volume 107, Issue 1-2

July, 2007

138 pages

ISSN:1077-3142

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2007.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 July 2007

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Cited By

Li MBai MLv Y(2022)Text segmentation by integrating hybrid strategy and non-text filteringMultimedia Tools and Applications10.1007/s11042-022-13029-181:30(44505-44522)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11042-022-13029-1
Zhu AUchida S(2019)Scene word recognition from pieces to wholeFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6420-213:2(292-301)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11704-017-6420-2
Yan CXie HChen JZha ZHao XZhang YDai Q(2018)A Fast Uyghur Text Detector for Complex Background ImagesIEEE Transactions on Multimedia10.1109/TMM.2018.283832020:12(3389-3398)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1109/TMM.2018.2838320
ari M(2017)Scene text segmentation using low variation extremal regions and sorting based character groupingNeurocomputing10.1016/j.neucom.2017.05.021266:C(56-65)Online publication date: 29-Nov-2017
https://dl.acm.org/doi/10.1016/j.neucom.2017.05.021
Sambyal NAbrol P(2016)Feature based Text Extraction System using Connected Component MethodInternational Journal of Synthetic Emotions10.4018/IJSE.20160101047:1(41-57)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.4018/IJSE.2016010104
Fan WBouguila N(2016)Model-Based Clustering Based on Variational Learning of Hierarchical Infinite Beta-Liouville Mixture ModelsNeural Processing Letters10.1007/s11063-015-9466-x44:2(431-449)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11063-015-9466-x
Qixiang Ye Doermann D(2015)Text Detection and Recognition in Imagery: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2014.236676537:7(1480-1500)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1109/TPAMI.2014.2366765
Zhu AWang GDong Y(2015)Detecting natural scenes text via auto image partition, two-stage grouping and two-layer classificationPattern Recognition Letters10.1016/j.patrec.2015.06.00967:P2(153-162)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1016/j.patrec.2015.06.009
Seok JKim J(2015)Scene text recognition using a Hough forest implicit shape model and semi-Markov conditional random fieldsPattern Recognition10.1016/j.patcog.2015.05.00448:11(3584-3599)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.patcog.2015.05.004
Zhao ZFang CLin ZWu Y(2015)A robust hybrid method for text detection in natural scenes by learning-based partial differential equationsNeurocomputing10.1016/j.neucom.2015.06.019168:C(23-34)Online publication date: 30-Nov-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.06.019
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