A New Edge-Based Method for Artificial Text Detection

Don Gao Zhou; Qi Cai Du; Qiang Lin; Jia Yu Lin

doi:10.4028/www.scientific.net/AMR.998-999.793

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 998-999A New Edge-Based Method for Artificial Text...

A New Edge-Based Method for Artificial Text Detection

Abstract:

In video indexing and retrieval systems, textual information is high-level semantic content to describe images or video frames, which is also one of the most interesting objects of study. A great progress has been made in text detection in recent decades, but it is still a great challenge due to the complex background, various fonts and different languages, etc. In this paper, we propose a edge-based approach to detect artificial text. First, the image edge map and difference range map are calculated. Then the map is binarized by local threshold method. Finally the textual information is located through the regional projection analysis. Experience results show our method’s precision rate is 91.1% and recall rate 93.2%, which outperforms the previous work.

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Periodical:

Advanced Materials Research (Volumes 998-999)

Pages:

793-796

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.998-999.793

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Online since:

July 2014

Authors:

Don Gao Zhou*, Qi Cai Du, Qiang Lin, Jia Yu Lin

Keywords:

Artificial Text, Edge Detection, Regional Analysis, Text Detection

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References

[1] K. Jung, K.I. Kim, A.K. Jain, Text Information Extraction in Images and Video: a Survey, J. Patt. Recogn. 37 (2004) 977-997.

DOI: 10.1016/j.patcog.2003.10.012

Google Scholar

[2] P. Shivakumara, T.Q. Phan, C.L. Tan, A Gradient Difference based Technique for Video Text Detection, IEEE ICDAR, (2009) 156-160.

Google Scholar

[3] M. Anthimopoulos, B. Gatos, I. Pratikakis, Detection of Artificial and Scene Text in Images and Video Frames, J. Patt. Anal. and Applic., 16 (2011) 431-446.

DOI: 10.1007/s10044-011-0237-7

Google Scholar

[4] R. Lienhart, A. Wernicke, Localizing and Segmenting Text in Images and Videos, J. IEEE Trans. on CSVT, 12 (2002) 256-268.

DOI: 10.1109/76.999203

Google Scholar

[5] M. R. Lyu, J.Q. Song, M. Cai, A Comprehensive Method for Multilingual Video Text Detection, Localization, and Extraction, IEEE Trans. on CSVT, 15 (2005) 243-255.

DOI: 10.1109/tcsvt.2004.841653

Google Scholar

[6] R.C. Gonzalez, R.E. Woods, Digital Image Processing, third ed., Publishing House of Electronics Industry, Beijing, 2011, 479-483.

Google Scholar

[7] J. Gllavata, R. Ewerth, B. Freisleben, A Robust Algorithm for Text Detection in Images, Proc. of the 3rd ISPA, (2003) 611-616.

DOI: 10.1109/ispa.2003.1296349

Google Scholar

[8] S.M. Lucas, ICDAR 2005 Text Locating Competition Results, IEEE ICDAR, (2005).

DOI: 10.1109/icdar.2005.231

Google Scholar

[9] D.T. Chen, H. Bourlard, J.P. Thiran, Text Identification in Complex Background Using SVM, CVPR, (2001) 621-626.

DOI: 10.1109/cvpr.2001.991021

Google Scholar