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

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

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    IMECS2017_pp410_414_Noise_Removal_and_Binarization_of_Scanned.pdf (1.470Mb)
    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
    Copyright Holder:
    Authors
    Available Online at:
    http://www.iaeng.org/publication/IMECS2017/
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Computing Conference Papers [147]

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