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In this chapter, we analyze several on-line cursive handwriting recognition systems. We find that virtually all such systems involve (a) a preprocessor, (b) a trainable classifier, and (c) a language modeling post-processor. Such architectures are described within the framework of Weighted Finite State Transductions, previously used in speech recognition by Pereira et al. We describe in some detail a recognition system built in our laboratory. It is a writer independent system which can handle a variety of writing styles including cursive script and handprint. The input to the system encodes the pen trajectory as a time-ordered sequence of feature vectors. A Time Delay Neural Network is used to estimate a posteriori probabilities for characters in a word. A Hidden Markov Model segments the word in a way which optimizes the global word score, taking a lexicon into account. The last part of the chapter is devoted to bibliographical notes.

Several significant sets of labeled samples of image data are surveyed that can be used in the development of algorithms for offline and online handwriting recognition as well as for machine printed text recognition. The method used to gather each data set, the numbers of samples they contain, and the associated truth data are discussed. In the domain of offline handwriting, the CEDAR, NIST, and CENPARMI data sets are presented. These contain primarily isolated digits and alphabetic characters. The UNIPEN data set of online handwriting was collected from a number of independent sources and it contains individual characters as well as handwritten phrases. The University of Washington document image databases are also discussed. They contain a large number of English and Japanese document images that were selected from a range of publications.