Noise removal and binarization of scanned document images using clustering of features

Farahmand, A.; Sarrafzadeh, Hossein; Shanbehzadeh, J.

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Date
2017-03
Citation:
Farahmand, A., Sarrafzadeh, A., & Shanbehzadeh, J. (2017, March). Noise Removal and Binarization of Scanned Document Images Using Clustering of Features. IMECS (Ed.), International MultiConference of Engineers and Computer Scientists (IMECS2017) (pp.410-414).
Permanent link to Research Bank record:
https://hdl.handle.net/10652/3879
Abstract
Old documents are in printed form. Their archiving and retrieval is expensive according in terms of space requirement and physical search. One solution is to convert these documents into electronic form using scanners. The outputs of scanners are images contaminated with noise. The outcomes are more storage requirement and low OCR accuracy. A solution is noise reduction. This paper employs KFCM algorithm to cluster pixels into text, background and noise according to their features. As a result, noise removal and binarization is done simultaneously.
Keywords:
preprocessing, document noise, binarization, noise removal algorithms, clustering
ANZSRC Field of Research:
080109 Pattern Recognition and Data Mining
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http://www.iaeng.org/publication/IMECS2017/
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