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An adaptive approach for Malay cheque words recognition using support vector machine


Al Boredi, Omar Noori Salih and Syed Ahmad Abdul Rahman, Sharifah Mumtazah and Shakil, Asma (2011) An adaptive approach for Malay cheque words recognition using support vector machine. Scientific Research and Essays, 6 (6). art. no. 6D71C7023325. pp. 1328-1336. ISSN 1992-2248


Support vector machines (SVMs) have played a significant role in the field of pattern recognition. This study utilizes the SVM as a classifier for the analysis of Malay cheque word recognition using Malay lexical database (Ahmad et al., 2007). The SVM system was used for individual character recognition and then lexical verification was applied for word level. Several pre-processing steps were taken such as noise removal, image normalization, and skeletonization prior to feature extraction to improve the dataset perspective and hence the recognition accuracy. Statistical and geometrical extraction techniques have been applied in the approach. The results show that the statistical feature is reliable, accessible and provides more accurate results. The results also show that the new approach passed 97.15% character recognition, and combined with word lexical verification, the recognition rate surpassed 98.2% recognition rate.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.5897/SRE10.1021
Publisher: Academic Journals
Keywords: Automated cheque processing; Character recognition; Lexical verification; Offline handwriting recognition; Pattern recognition; Support vector machine
Depositing User: Nabilah Mustapa
Date Deposited: 07 Dec 2015 02:14
Last Modified: 18 Oct 2018 07:04
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.5897/SRE10.1021
URI: http://psasir.upm.edu.my/id/eprint/23119
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