Gabor filter based multi-class classifier for scanned document images
Huanfeng Ma, David Doermann · 2005
When scanning documents with a large number of pagessuch as books, it is often feasible to provide a minimalnumber of training samples to personalize the system tocompensate for global shifts in how the document wascreated or in scanning parameters. In this paper, wepresent a supervised multi-class classifier based onGabor filters that is used to classify the scripts, font-faces,and font-styles (bold, italic, normal etc.) in anapplication where the classes are known. Classificationis performed at the word level (glyphs separated by whitespace) given training samples of each class. This methodwas applied to a variety of bilingual dictionaries toidentify different scripts, and simultaneously, to classifyRoman scripts into bold, italic and normal font-styles.Experimental results show the effectiveness of thisapproach in increasing performance over classifierstrained for general documents.