Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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Research Article, J Comput Eng Inf Technol Vol: 4 Issue: 2

Handwritten Devanagari Numeral Recognition by Fusion of Classifiers

Prabhanjan S1* and Dinesh R2
1Research Scholar, School of Engineering and Technology, Jain University, Bangalore, India
2Research Supervisor, School of Engineering and Technology, Jain University, Bangalore, India
Corresponding author : Prabhanjan S
Research Scholar, School of Engineering and Technology, Jain University, Bangalore, India
Tel: 8123674234; E-mail: [email protected], [email protected]
Received: April 19, 2015 Accepted: June 29, 2015 Published: July 06, 2015
Citation: Prabhanjan S, Dinesh R (2015) Handwritten Devanagari Numeral Recognition by Fusion of Classifiers. J Comput Eng Inf Technol 4:2. doi:10.4172/2324-9307.1000128

Abstract

Handwritten Devanagari Numeral Recognition by Fusion of Classifiers

Recognition of handwritten Devanagari numerals has many applications especially in the field of postal automation, document processing and so on. Due to its vast applications, many researchers are actively working towards development of effective and efficient hand written character/numeral recognition. Devanagari script is widely used script in Indian sub-continent; also Devanagari script forms the basis for many other scripts in Indian sub-continent. In this paper, we have proposed a hybrid method to recognize handwritten Devanagari numerals. The proposed method uses, stacking approach to fuse the confidence scores from four different classifiers viz., Naïve Bayes (NB), Instance Based Learner (IBK), Random Forest (RF), Sequential Minimal Optimization (SMO). Also, the proposed method extracts both local and global features from the handwritten numerals. In this work, we have used Fourier Descriptors as global shape feature. Whereas, the pixel density statistics from different zones of the numeral to describe the numerals locally. The proposed method has been tested on large set of handwritten numeral database and experimental results reveal that the proposed method yields the accuracy of 99.685%, which is the best accuracy reported so far for the datasets considered. Hence the proposed method outperforms contemporary algorithms.

Keywords: Classifiers; Handwritten numeral recognition; Fourier descriptor; Machine learning; Stacking

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