Gabor Filter Based Multi-class Classifier for Scanned Document Images

When scanning documents with a large number of pagessuch as books, it is often feasible to provide a minimal
number of training samples to personalize the system to
compensate for global shifts in how the document was
created or in scanning parameters. In this paper, we
present a supervised multi-class classifier based on
Gabor filters that is used to classify the scripts, font-faces,
and font-styles (bold, italic, normal etc.) in an
application where the classes are known. Classification
is performed at the word level (glyphs separated by white
space) given training samples of each class. This method
was applied to a variety of bilingual dictionaries to
identify different scripts, and simultaneously, to classify
Roman scripts into bold, italic and normal font-styles.
Experimental results show the effectiveness of this
approach in increasing performance over classifiers
trained for general documents.