Author:
Mattias Wahde
Affiliation:
Chalmers University of Technology, Sweden
Keyword(s):
Document Image Binarization, Image Processing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Vision and Perception
Abstract:
In this paper, a new method for binarization of document images is introduced. During training, the method
stores histograms from training images (divided into small tiles), along with the optimal binarization threshold.
Training image tiles are presented in pairs, one noisy version and one clean binarized version, where the
latter is used for finding the optimal binarization threshold. During use, the method considers the tiles of an
image one by one. It matches the stored histograms to the histogram for the tile that is to be binarized. If a
sufficiently close match is found, the tile is binarized using the corresponding threshold associated with the
stored histogram. If no match is found, the contrast of the tile is slightly enhanced, and a new attempt is made.
This sequence is repeated until either a match is found, or a (rare) timeout is reached. The method has been
applied to a set of test images, and has been shown to outperform several comparable methods.