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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| Published in: | IEEE transactions on pattern analysis and machine intelligence Vol. 33; no. 4; pp. 767 - 779 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Los Alamitos, CA
IEEE
01.04.2011
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0162-8828, 1939-3539, 1939-3539 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Gorbe-Moya, J España-Boquera, S Castro-Bleda, M J Zamora-Martinez, F |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models |
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