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A Study of Moment Based Features on Handwritten Digit Recognition
Joint Authors
Nasipuri, Mita
Singh, Pawan Kumar
Sarkar, Ram
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-10
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Information Technology and Computer Science
Abstract EN
Handwritten digit recognition plays a significant role in many user authentication applications in the modern world.
As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem.
A lot of work has been done for various non-Indic scripts particularly, in case of Roman, but, in case of Indic scripts, the research is limited.
This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu.
A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendre moment, Zernike moment, and complex moment, has been estimated for each digit sample.
Finally, the technique is evaluated on CMATER and MNIST databases using multiple classifiers and, after performing statistical significance tests, it is observed that Multilayer Perceptron (MLP) classifier outperforms the others.
Satisfactory recognition accuracies are attained for all the five mentioned scripts.
American Psychological Association (APA)
Singh, Pawan Kumar& Sarkar, Ram& Nasipuri, Mita. 2016. A Study of Moment Based Features on Handwritten Digit Recognition. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1094896
Modern Language Association (MLA)
Singh, Pawan Kumar…[et al.]. A Study of Moment Based Features on Handwritten Digit Recognition. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-17.
https://search.emarefa.net/detail/BIM-1094896
American Medical Association (AMA)
Singh, Pawan Kumar& Sarkar, Ram& Nasipuri, Mita. A Study of Moment Based Features on Handwritten Digit Recognition. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1094896
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094896