DEVELOPMENT OF A HYBRID PSO-SVM-BASED OFFLINE OPTICAL CHARACTER RECOGNITION SYSTEM
Journal Title: World Journal of Engineering Research and Technology - Year 2018, Vol 4, Issue 3
Abstract
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.
Authors and Affiliations
Oyeleye Christopher Akinwale
Keywords
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