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Document Image Binarisation Using A Supervised Neural Network. (Khashman, Adnan,)
Bibliographical information (record 269924)
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Document Image Binarisation Using A Supervised Neural Network.
Author:
 Khashman, Adnan, Search Author in Amazon Books

Publisher:
World Scientific,
Edition:
 2008.
Classification:
 QA76
URL:
http://library.neu.edu.tr:2048/login?url=http://dx.doi.org/10.1142/S0129065708001671
Detailed notes
    - Advances in digital technologies have allowed us to generate more images than ever. Images of scanned documents are examples of these images that form a vital part in digital libraries and archives. Scanned degraded documents contain background noise and varying contrast and illumination, therefore, document image binarisation must be performed in order to separate foreground from background layers. Image binarisation is performed using either local adaptive thresholding or global thresholding; with local thresholding being generally considered as more successful. This paper presents a novel method to global thresholding, where a neural network is trained using local threshold values of an image in order to determine an optimum global threshold value which is used to binarise the whole image. The proposed method is compared with five local thresholding methods, and the experimental results indicate that our method is computationally cost-effective and capable of binarising scanned degraded documents with superior results.
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Status
Library
Section
EOL-1730
Item available
NEU Grand LibraryOnline (QA76 .D63 2008)
Online electronic
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