In this paper, the problem of spam filtering for images, a type of fast-spreading spam where the text is included in images to overcome the text-based spam filter. One common method for detecting spam is the optical character recognition system (OCR) that detecting and recognizing the text embedded, following by a classifier which distinguishes spam from ham. Nevertheless, the spammers begin hiding image text for preventing OCR from detecting spam. To recompense for the shortages of the OCR system, a method based on the detection algorithm is proposed for the text region. To estimate the performance of the projected system, the methodology was applied to a group of unwanted images Dredze (available to the public) to check the efficiency of our method which outperforms the initial OCR system in sensible use with a complex background in spam. The test results indicated that the new method gives good text regions detection even for noisy images.

Keywords
OCR Text localization Spam image Text-based spam filtering Text features
This is a preview of subscription content, log in to check access.
References
1.
Gupta, Y., Sharma, S.H., Bedwal, T.: Text extraction techniques. Int. J. Comput. Appl. NSFTICE, 10–12 (2015)
Google Scholar
2.
Natei, K.N., Viradiya, J., Sasikumar, S.: Extracting text from image document and displaying its related information. J. Eng. Res. Appl. 8(5), 27–33 (Part-V) (2018). ISSN: 2248-9622
Google Scholar
3.
Mathur, G., Rikhari, S.: Text detection in document images: highlight on using FAST algorithm. Int. J. Adv. Eng. Res. Sci. (IJAERS) 4(3) (2017). ISSN: 2349-6495(P)|2456-1908(O)
CrossRefGoogle Scholar
4.
Kulkarni, C.R., Barbadekar, A.B.: Text detection and recognition: a review. Int. Res. J. Eng. Technol. (IRJET) (2017). e-ISSN: 2395-0056, p-ISSN: 2395-0072
Google Scholar
5.
Dai, J., Wang, Z., Zhao, X., Shao, S.: Scene text detection based on enhanced multi-channels MSER and a fast text grouping process. Int. J. Comput. Linguist. Res. 9(2) (2018)
Google Scholar
6.
Lee, H.: Wavelet analysis for image processing. Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan. On http://disp.ee.ntu.edu.tw/henry/wavelet_analysis.pdf
7.
Javed, M., Nagabhushan, P., Chaudhuri, B.B.: Extraction of projection profile, run-histogram and entropy features straight from run-length compressed text documents. In: IAPR Asian conference on Pattern Recognition, IEEE proceedings, pp. 813–817 (2013)
Google Scholar
8.
Burger, W., Burge, M.J.: Principles of digital image processing. Cor Algorithms. Springer Publishing Company (2009)
Google Scholar
9.
Gonz´alez, A., Bergasa, L.M., Yebes, J.J., Bron, S.: Text location in complex images. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), pp. 617–620, Tsukuba, 11–15 Nov 2012
Google Scholar
10.
From https://www.cs.jhu.edu/~mdredze/datasets/image_spam/
Copyright information
© Springer Nature Singapore Pte Ltd. 2020
About this paper
CrossMark
Cite this paper as:
Dhahi E.H., Ali S.A., Naser M.A. (2020) Text Region Extraction for Noisy Spam Image. In: Mallick P., Balas V., Bhoi A., Chae GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-15-1451-7_25
First Online
15 January 2020
DOI
https://doi.org/10.1007/978-981-15-1451-7_25
Publisher Name
Springer, Singapore
Print ISBN
978-981-15-1450-0
Online ISBN
978-981-15-1451-7
eBook Packages
Intelligent Technologies and Robotics
Intelligent Technologies and Robotics (R0)
Buy this book on publisher s site
Reprints and Permissions
Actions
Log in to check access
Buy eBook
EUR 213.99
Buy paper (PDF)
EUR 24.95
Instant download
Readable on all devices
Own it forever
Local sales tax included if applicable
Buy Physical Book
Learn about institutional subscriptions

Cite paper
How to cite?
.RIS
Papers
Reference Manager
RefWorks
Zotero
.ENW
EndNote
.BIB
BibTeX
JabRef
Mendeley
Over 10 million scientific documents at your fingertips
Academic Edition
Corporate Edition
Home Impressum Legal information Privacy statement California privacy statement How we use cookies Manage cookies/Do not sell my data Accessibility Contact us
Springer Nature
© 2020 Springer Nature Switzerland AG. Part of Springer Nature.

Not logged in Not affiliated 37.239.72.9