20. A method for training a handwritten document categorizer comprising: defining a taxonomy comprising a set of categories; for each of the categories in the set of categories, extracting a set of discriminative words, from a training set of typed documents labeled by category from the set of categories, the extracting of the set of discriminative words including computing frequencies of words in the labeled typed documents and, based on the word frequencies, identifying words whose frequency of occurrence in typed documents labeled with the category differs from a frequency of occurrence in typed documents labeled with other categories or for all the categories; for each keyword in a group of keywords, synthesizing a set of computer-generated samples of the keyword with a plurality of different type fonts, the keywords comprising at least one discriminative word for each category in the set of categories, wherein for each type font, each letter of the keyword is generated from a stored character set comprising letters of the alphabet; generating a keyword model for each keyword in the group, parameters of the model being estimated based on features extracted from the synthesized samples of that keyword; computing keyword statistics for each of a set of scanned handwritten documents, each of the documents being labeled by category selected from the set of categories, by applying the generated keyword models for the group of keywords to word images extracted from the scanned handwritten documents, the computing of the statistics for each scanned document including computing the number of occurrences of each keyword in the document, the statistics being based on the computed number of occurrences; and with a computer processor, training a handwritten document categorizer with the keyword statistics computed for the set of handwritten documents and the respective handwritten document labels.
20. A method for training a handwritten document categorizer comprising: defining a taxonomy comprising a set of categories; for each of the categories in the set of categories, extracting a set of discriminative words, from a training set of typed documents labeled by category from the set of categories, the extracting of the set of discriminative words including computing frequencies of words in the labeled typed documents and, based on the word frequencies, identifying words whose frequency of occurrence in typed documents labeled with the category differs from a frequency of occurrence in typed documents labeled with other categories or for all the categories; for each keyword in a group of keywords, synthesizing a set of computer-generated samples of the keyword with a plurality of different type fonts, the keywords comprising at least one discriminative word for each category in the set of categories, wherein for each type font, each letter of the keyword is generated from a stored character set comprising letters of the alphabet; generating a keyword model for each keyword in the group, parameters of the model being estimated based on features extracted from the synthesized samples of that keyword; computing keyword statistics for each of a set of scanned handwritten documents, each of the documents being labeled by category selected from the set of categories, by applying the generated keyword models for the group of keywords to word images extracted from the scanned handwritten documents, the computing of the statistics for each scanned document including computing the number of occurrences of each keyword in the document, the statistics being based on the computed number of occurrences; and with a computer processor, training a handwritten document categorizer with the keyword statistics computed for the set of handwritten documents and the respective handwritten document labels. 23. The method of claim 20 , wherein there are up to 20 of the categories.