Content-based document enhancement by fuzzy clustering with spatial constraints

Mohamed Nooman Ahmed; Brian E. Cooper

doi:10.1117/12.585543

23 February 2005 Content-based document enhancement by fuzzy clustering with spatial constraints

Proceedings Volume 5673, Applications of Neural Networks and Machine Learning in Image Processing IX; (2005) https://doi.org/10.1117/12.585543
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

In this paper, we present a new system to segment and label the contents of scanned documents as either text or image, using a modified fuzzy c-means (FCM) algorithm. Each pixel is assigned a feature pattern extracted from the gray level distribution and computed at different scales. The invariant feature pattern is then assigned to a specific region using fuzzy logic. Our algorithm is formulated by modifying the objective function of the standard FCM algorithm to allow the labeling of a pixel to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by scanner noise.

Citation Download Citation

Mohamed Nooman Ahmed and Brian E. Cooper "Content-based document enhancement by fuzzy clustering with spatial constraints", Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005); https://doi.org/10.1117/12.585543

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KEYWORDS

Image segmentation

Fuzzy logic

Image processing algorithms and systems

Scanners

Feature extraction

Image enhancement

Halftones

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