Principal components analysis for Hindi digits recognition

Citation

El-Bashir, Mohammad Said Mansur and O. K. Rahmat, Rahmita Wirza and Ahmad, Fatimah and Sulaiman, Md. Nasir (2008) Principal components analysis for Hindi digits recognition. In: International Conference on Computer and Communication Engineering 2008 (ICCCE08), 13-15 May 2008, Kuala Lumpur, Malaysia. (pp. 738-740).

Abstract

The recognition process depends on the how features are extracted. There are several ways for feature extraction but the most important is to extract the most effective features and can distinct between patterns. In this research, an approach is proposed to recognize Hindi numerals. Initially image is enhanced and normalized. After that, PCA is applied for feature extraction. Recognition is performed by using first and second Norm. Another two more norms were proposed named ENorm and EEuclidean. Results showed 93.5%, 94.79%, 95% and 94.79% recognition accuracy when applying first norm, ENorm, second norm and EEuclidean respectively.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.1109/ICCCE.2008.4580702
Publisher:	IEEE
Keywords:	Principal components analysis; Hindi digits recognition
Depositing User:	Nabilah Mustapa
Date Deposited:	10 May 2019 08:31
Last Modified:	10 May 2019 08:31
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICCCE.2008.4580702
URI:	http://psasir.upm.edu.my/id/eprint/68338
Statistic Details:	View Download Statistic

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