Adaptation de modèles de Markov cachés - Application à la reconnaissance de caractères imprimés

International audienceWe present in this paper a new algorithm for the adaptation of hidden Markov models (HMM models). The principle of our iterative adaptive algorithm is to alternate an HMM structure adaptation stage with an HMM Gaussian MAP adaptation stage. This algorithm is applied to the recognition of printed characters to adapt the models learned by a polyfont character recognition engine to new forms of characters. Comparing the results with those of MAP and MLLR classic adaptations shows a slight increase in the performance of the recognition system

Techniques d'adaptation de modèles markoviens. Application à la reconnaissance de documents anciens

Ce travail s'intéresse à la reconnaissance de caractères dans les documents imprimés. Le but est de créer un OCR suffisamment robuste pour être performant sur les documents anciens dont les particularités les rendent difficiles à traiter par les OCRs. Nous avons créé un système de reconnaissance polyfonte basé sur des modèles de Markov cachés (MMC) et nous l'avons intégré dans une chaîne de traitement OCR complète en utilisant des outils logiciels libres. Afin d'améliorer les performances de ce système sur de nouvelles données, nous avons créé des algorithmes d'adaptation qui modifient conjointement la structure et les probabilités d'émission des MMC. Nous avons évalué le système de reconnaissance polyfonte ainsi que les algorithmes d'adaptation sur des bases d'images réelles et synthétiques. Les résultats obtenus montrent que le système de reconnaissance polyfonte est compétitif comparé aux systèmes d'OCR industriels et que nos algorithmes d'adaptation de la structure devancent nettement les algorithmes d'adaptation de l'état de l'art.This work focuses on the recognition of characters in printed documents. The goal is to create a sufficiently robust OCR system that can deal with ancient documents whose peculiarity makes them difficult to process. We created a polyfont recognition system based on Hidden Markov Models (HMM) and we have integrated it into a complete processing chain using open source OCR tools. To improve the performance of this system on new data, we created new adaptation algorithms that jointly modify the structure and emission probabilities of HMMs. We evaluated the polyfont recognition system and the adaptation algorithms on synthetic and real images datasets. The results show that the polyfont recognition system is competitive compared to commercial OCR systems and that our structure-adaptation algorithms are more efficient than other state of the art adaptation algorithms.ROUEN-BU Sciences Madrillet (765752101) / SudocSudocFranceF

Combining Structure and Parameter Adaptation of HMMs for Printed Text Recognition

International audienceWe 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

Prédiction de la performance des systèmes d'OCR

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Structure Adaptation of HMM applied to OCR

International audienceIn this paper we present a new algorithm for the adaptation of Hidden Markov Models (HMM models). The principle of our iterative adaptive algorithm is to alternate an HMM structure adaptation stage with an HMM Gaussian MAP adaptation stage of the parameters. This algorithm is applied to the recognition of printed characters to adapt the character models of a poly font general purpose character recognizer to new fonts of characters, never seen during training. A comparison of the results with those of MAP classical adaptation scheme show a slight increase in the recognition performance

Adaptation d'un système d'OCR Markovien à de nouvelles fontes

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Font adaptation of an HMM-based OCR system

International audienceWe create a polyfont OCR recognizer using HMM (Hidden Markov models) models of character trained on a dataset of various fonts. We compare this system to monofont recognizers showing its decrease of performance when it is used to recognize unseen fonts. In order to fill this gap of performance, we adapt the parameters of the models of the polyfont recognizer to a new dataset of unseen fonts using four different adaptation algorithms. The results of our experiments show that the adapted system is far more accurate than the initial system although it does not reach the accuracy of a monofont recognizer

Maritime shifts in the world economy: evidence from the Lloyd's List corpus, eighteenth to twenty-first centuries

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A formal model for output multimodal HCI - An Event-B formalization

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