Handwritten Word Recognition with Contextual Hidden Markov Models

An approach to handwritten word recognition is described which attempts to combine the properties of hidden Markov modeling with those of segmentation-by-recognition. The approach is based on a heuristic segmentation of word images which is designed to find all characters frontiers and which tends to propose also superfluous segmentation cuts. Bitmaps delimited by neighboring cut hypotheses are input to a character recognition module whose outputs serve as the observations of the hidden Markov model. Through the use of a character recognizer applied to recombined bitmaps, the method is allowed to recover the identity of pseudo-characters using contextual information. By considering the recognizer outputs as HMM observations instead of directly using them in a scoring function, the approach inherits the well-founded and efficient estimation algorithms of hidden Markov models.