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World Journal of Engineering
Research and Technology

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.218

ICV : 79.45

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    JANUARY 2019 Issue has been successfully launched on 1 January 2019




Oyeleye Christopher Akinwale*, Alo Oluwaseun Olubisi, Sijuade Adeyemi, Alade Oluwaseun Modupe, Akinpelu James Abiodun, Egbetola Funmilola Ikeolu


Optical Character Recognition (OCR) is the mechanical or electronic translation of images of handwritten, typewritten or printed text into machine-editable text. However, with support vector machine, it is difficult to take the proper threshold value and thus end up losing the necessary pixels. On the other hand, SVM only supports atomic concepts rather than complex concepts which makes its applications partially limited. This further makes it difficult to take the scan sample of the forms which includes lots of boxes. In addition, with large size / dimension of features, support vector machine often produce Inaccurate classification results Therefore in this paper, a hybrid of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is developed for optical handwritten character recognition. The PSO was used for feature extraction and dimensionality reduction of the resultant features while SVM was used for classification at both training and testing stages. The performance of developed hybrid PSOSVM for OCR was evaluated using accuracy and time. The evaluation results when compared with support vector machine reveal that the developed hybrid PSO-SVM for OCR outperforms the conventional SVM.

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