Volume 11 : Number 2 : Paper 2 |
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December 2008 Special Issue of Best Papers presented at CLEI 2007, San Jose, Costa Rica
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Title:
Postal Envelope Segmentation using Learning-Based Approach
Authors and Affiliations:
Benjamin Baran, National Computing Center, National University of Asuncion, Paraguay
Jacques Facon, PUCPR-Curitiba, PR, Brazil
Horacio Legal-Ayala, Universidad Nacional de Asuncion, Paraguay
Abstract:
This paper presents a learning-based approach to segment postal address blocks where the learning step uses only one pair of images (a sample image and its ideal segmented solution). First, this approach learns the available knowledge among pixels (each gray level) in an input image and its corresponding output in the ideal segmented solution. A classification array is generated which is re-utilized during the segmentation of new images. Features are extracted and updated by means of an adaptive square neighborhood. At the moment of new image segmentation, the submitted images are segmented by means
of a k-Nearest Neighbor (k-NN) algorithm that seeks, for each pixel, the best solution in the classification array. Tests on a database of 200 complex envelope images were performed and a pixel to pixel accuracy measure validates the new approach. Results compared to other approaches for the same database show the efficiency and performance of the proposed learning-based approach. Success rates achieved for address block, stamps, rubber stamps and noise suggest that the features used in the proposed approach improves the segmentation results.
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Received April, 2007, Revised dec, 2008
, Editor: Manuel Bermudez, Marcelo Jenkins
Full paper, 12 pages
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PDF, 507 Kb ]
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