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Telugu character recognition for degraded palm leaf documents using optimal feature selection techniques - a 3D approach

Telugu character recognition for degraded palm leaf documents using optimal feature selection... Palm leaves were used as a medium of recording information about 700 years ago. This work deals with the recognition of Telugu palm leaf characters by acquiring 3D data using a contact-type profiler. A novel concept of using a 3D inherent feature, i.e. depth of incision is proposed to eliminate noise. With the help of this 3D feature, improved recognition accuracy is also reported for various features extracted from the palm leaf characters. Experiments are conducted by implementing optimisation techniques, such as differential evolution and particle swarm optimisation, to find the optimum number of features to reduce the memory needed. With varying feature dimensions, average classification accuracies are reported for combination of feature extraction methods and optimisation techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Telugu character recognition for degraded palm leaf documents using optimal feature selection techniques - a 3D approach

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2017.087764
Publisher site
See Article on Publisher Site

Abstract

Palm leaves were used as a medium of recording information about 700 years ago. This work deals with the recognition of Telugu palm leaf characters by acquiring 3D data using a contact-type profiler. A novel concept of using a 3D inherent feature, i.e. depth of incision is proposed to eliminate noise. With the help of this 3D feature, improved recognition accuracy is also reported for various features extracted from the palm leaf characters. Experiments are conducted by implementing optimisation techniques, such as differential evolution and particle swarm optimisation, to find the optimum number of features to reduce the memory needed. With varying feature dimensions, average classification accuracies are reported for combination of feature extraction methods and optimisation techniques.

Journal

International Journal of Signal and Imaging Systems EngineeringInderscience Publishers

Published: Jan 1, 2017

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