Noise removal and binarization of scanned document images using clustering of features
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Authors
Farahmand, Atena
Sarrafzadeh, Hossein
Shanbehzadeh, J.
Sarrafzadeh, Hossein
Shanbehzadeh, J.
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Date
2017-03
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Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
preprocessing
document noise
binarization
noise removal algorithms
clustering
document noise
binarization
noise removal algorithms
clustering
ANZSRC Field of Research Code (2020)
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).
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.
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Newswood and International Association of Engineers
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