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Handwriting Recognition: Soft Computing and Probabilistic Approaches

Liu, Zhi-Chang, Cai, Jinhai, & Buse, Richard (2003) Handwriting Recognition: Soft Computing and Probabilistic Approaches. Studies in Fuzziness and Soft Computing. Springer, New York.

Abstract

In this book, we introduce several methods for recognising unconstrained handwritten words and digits using hidden Markov models (HMMs) and Markov random field (MRF) models. Since the hidden Markov model (HMM) is stochastic finite state automation, it is able to represent a sequence of features. we used HMMs to model features that are extracted from outer contours of images to form sequences. To overcome the limitation of HMMs in modelling structural information, we used structural models, which are based on the best sequences of states, to represent structural information and enhance the performance of HMMs. In addition, we presented a procdure to model relationships between spectral components using 2-D HMMs, where the spectral features are extracted by Fourier descriptor. This method can be used to recognise two-dimensional shapes as well as handwritten digits.

Markov random field models are appropriate to model two-dimensional features of handwritten words and digits. The most important merit of Markov random field models is that they provide flexible and natural methods for modelling the interaction between spatially related random variables in their neighbourhood systems via designed clique functions. In MRF model, the global optimum can be derived from local information in term of clique functions. This book also describes methods to use MRFs to model structural relationships between line-segments for recognising handwritten words and to model both structural and statistical information for recognising handwritten digits. Relaxation labelling is used to maximise the global compatibility of MRF models.

To evaluate the proposed methods, we had conducted experiments on two databases: handwritten word database and handwritten digit database. Both databases are taken from USPS CEDAR CDROM1. The recognition rates for handwritten words are from 69.0% to 96.5% among top 1 to top 5 positions with only 7.5 training images per word on the average. The recognition rates for handwritten digits range from 96.48\% to 98.37% with different methods. These results show our method can achieve recognition rates comparable to that reported in the literature recently.

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198 since deposited on 28 Mar 2006
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ID Code: 3732
Item Type: Book
Additional Information: For more information about this book please refer to the publisher's website (see link) or contact the author. Author contact details : j.cai@qut.edu.au
Additional URLs:
Keywords: Handwriting Recognition, hidden Markov model, Fuzzy logic, Markov random field
ISBN: 9783540401773
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2003 Springer
Deposited On: 28 Mar 2006
Last Modified: 29 Feb 2012 22:58

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