1. Background, applications and modeling
Hidden Markov Models (HMM) are statistical models defined by a set of parameters that are learned from a corpus of training data. They implement a probability density on sequential data.
1.1 Illustration of an MMC application
Figure 1 illustrates the main application of MMCs for handwriting recognition. MMC-based systems use an image of a handwritten word to identify the sequence of characters making it up, which means determining the number of characters, recognizing which characters they are, and their start and end positions (abscissae) in the image.
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Background, applications and modeling
Markov chains and probabilistic finite-state automata
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