Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models

This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 33; H. 4; S. 767 - 779
Hauptverfasser: España-Boquera, S, Castro-Bleda, M J, Gorbe-Moya, J, Zamora-Martinez, F
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.04.2011
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Artificial intelligence
Artificial neural networks
Automatic Data Processing - methods
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Handwriting
Handwriting recognition
Hidden Markov models
HMM
Humans
hybrid HMM/ANN
image normalization
Image segmentation
Learning theory
Markov Chains
Markov processes
Mathematical models
multilayer perceptron
Multilayer perceptrons
Neural networks
offline handwriting
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Pixel
Reading
Recognition
Reproducibility of Results
Text recognition
Texts
On line
Probabilistic approach
Image processing
image normalization
neural networks
HMM
Text
Markov model
Pattern recognition
Neural network
Character recognition
hybrid HMM/ANN
Modeling
Handwriting recognition
Supervised learning
Optical character recognition
Database
Hidden Markov model
Multilayer perceptrons
multilayer perceptron
Hybrid model
Feedforward
Manuscript character
offline handwriting
ISSN:0162-8828, 1939-3539, 1939-3539
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission probabilities. This paper also presents new techniques to remove slope and slant from handwritten text and to normalize the size of text images with supervised learning methods. Slope correction and size normalization are achieved by classifying local extrema of text contours with Multilayer Perceptrons. Slant is also removed in a nonuniform way by using Artificial Neural Networks. Experiments have been conducted on offline handwritten text lines from the IAM database, and the recognition rates achieved, in comparison to the ones reported in the literature, are among the best for the same task.
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ISSN:0162-8828
1939-3539
1939-3539
DOI:10.1109/TPAMI.2010.141