1. 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, 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; 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, wherein each synthesized typed sample comprises a computer-generated binary image of a fixed pixel height and wherein the extracted handwritten word images are normalized to the same fixed pixel height and are binary images; computing keyword statistics for each of a set of scanned handwritten documents labeled by category 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, each keyword model outputting a probability of word images in the respective handwritten document matching the respective keyword, frequencies for each keyword being computed as a function of the probabilities, the statistics being based on the computed frequencies; 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 category labels.