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Automatic filter selection using image quality assessment

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4 Author(s)
Souza, A. ; Centre for Pattern Recognition & Machine Intelligence, Concordia Univ., Montreal, Que., Canada ; Cheriet, M. ; Naoi, S. ; Suen, C.Y.

We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.

Published in:

Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on

Date of Conference:

3-6 Aug. 2003

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