The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
DL '97 Proceedings of the second ACM international conference on Digital libraries
Font Recognition Based on Global Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Characterization and Classification of Printed Text in a Multiscale Context
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
IBM Journal of Research and Development
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In this article we present a particular application of Gabor filtering for machine-printed document image understanding. To do so, we assume that the text can be seen as texture, characters being the smallest texture elements, and we verify this hypothesis by a series of experiments over different sets of character images. We first apply a bank of 24 Gabor filters (4 frequencies and 6 orientations) on each set, then we extract texture features, that are used to classify character images without a priori knowledge using a Bayesian classifier. Results are shown for different characters written in a same font, and for different font types given a character.