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. © Springer-Verlag 2003.
CITATION STYLE
Allier, B., & Emptoz, H. (2003). Character prototyping in document images using gabor filters. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2749, 28–35. https://doi.org/10.1007/3-540-45103-x_5
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