Principal components analysis for Hindi digits recognition
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...
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2008
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Online Access: | http://psasir.upm.edu.my/id/eprint/68338/1/Principal%20components%20analysis%20for%20Hindi%20digits%20recognition.pdf http://psasir.upm.edu.my/id/eprint/68338/ |
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my.upm.eprints.683382019-05-10T08:31:48Z http://psasir.upm.edu.my/id/eprint/68338/ Principal components analysis for Hindi digits recognition El-Bashir, Mohammad Said Mansur O. K. Rahmat, Rahmita Wirza Ahmad, Fatimah Sulaiman, Md. Nasir 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. IEEE 2008 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68338/1/Principal%20components%20analysis%20for%20Hindi%20digits%20recognition.pdf 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). 10.1109/ICCCE.2008.4580702 |
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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. |
format |
Conference or Workshop Item |
author |
El-Bashir, Mohammad Said Mansur O. K. Rahmat, Rahmita Wirza Ahmad, Fatimah Sulaiman, Md. Nasir |
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El-Bashir, Mohammad Said Mansur O. K. Rahmat, Rahmita Wirza Ahmad, Fatimah Sulaiman, Md. Nasir Principal components analysis for Hindi digits recognition |
author_facet |
El-Bashir, Mohammad Said Mansur O. K. Rahmat, Rahmita Wirza Ahmad, Fatimah Sulaiman, Md. Nasir |
author_sort |
El-Bashir, Mohammad Said Mansur |
title |
Principal components analysis for Hindi digits recognition |
title_short |
Principal components analysis for Hindi digits recognition |
title_full |
Principal components analysis for Hindi digits recognition |
title_fullStr |
Principal components analysis for Hindi digits recognition |
title_full_unstemmed |
Principal components analysis for Hindi digits recognition |
title_sort |
principal components analysis for hindi digits recognition |
publisher |
IEEE |
publishDate |
2008 |
url |
http://psasir.upm.edu.my/id/eprint/68338/1/Principal%20components%20analysis%20for%20Hindi%20digits%20recognition.pdf http://psasir.upm.edu.my/id/eprint/68338/ |
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