Structure Adaptation of HMM Applied to OCR

Mohand, Kamel Ait; Paquet, Thierry; Ragot, Nicolas

doi:10.1109/icpr.2010.705

Cited by 2 publications

(1 citation statement)

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“…This iterative algorithm [AMPRH10] directs the adaptation of the HMM in order to maximize a likelihood criterion. As the validation dataset is different from the data used for re-estimating the model parameters, it is possible to use a likelihood measure, estimated by the recognition system, as a guiding criterion.…”

Section: Model Selection Based Structure Adaptation (Ms-sa)mentioning

confidence: 99%

Combining Structure and Parameter Adaptation of HMMs for Printed Text Recognition

Ait-Mohand

Paquet

Ragot

2014

IEEE Trans. Pattern Anal. Mach. Intell.

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We present two algorithms that extend existing HMM parameter adaptation algorithms (MAP and MLLR) by adapting the HMM structure. This improvement relies on a smart combination of MAP and MLLR with a structure optimization procedure. Our algorithms are semi-supervised: to adapt a given HMM model on new data, they require little labeled data for parameter adaptation and a moderate amount of unlabeled data to estimate the criteria used for HMM structure optimization. Structure optimization is based on state splitting and state merging operations and proceeds so as to optimize either the likelihood or a heuristic criterion. Our algorithms are successfully applied to the recognition of printed characters by adapting the HMM character models of a polyfont printed text recognizer to new fonts. Our experiments involve a total of 1,120,000 real and 3,100,000 synthetic character images and concern a set of 89 HMM models. A comparison of our results with those of state-of-the-art adaptation algorithms (MAP and MLLR) shows a significant increase in the accuracy of character recognition.

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Section: Model Selection Based Structure Adaptation (Ms-sa)mentioning

confidence: 99%