In this paper, a new feature extraction operator, the grating cell operator, is applied to analyze the texture features and classify different fonts of scanned document images. This operator is compared with the isotropic Gabor filter feature extractor which was also employed to classify fonts of documents. In order to improve the performance, a back-propagation neural network (BPNN) classifier is applied to the extracted features to perform the classification and compared with the simple weighted Euclidean distance (WED) classifier. Experimental results show that the grating cell operator performs better than the isotropic Gabor filter, and the BPNN classifier can provide more accurate classification results than the WED classifier.

Date Published: 17 January 2005
PDF: 9 pages
Proc. SPIE 5676, Document Recognition and Retrieval XII, (17 January 2005); doi: 10.1117/12.586345

Published in SPIE Proceedings Vol. 5676:
Document Recognition and Retrieval XII
Elisa H. Barney Smith; Kazem Taghva, Editor(s)