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PCA Based Handwritten Character Recognition System Using Support Vector Machine & Neural Network

Ravi Sheth, N C Chauhan, Mahesh M Goyani, Kinjal A Mehta


Pattern recognition deals with categorization of input data into one of the given classes based on extraction of features. Handwritten Character Recognition (HCR) is one of the well-known applications of pattern recognition. For any recognition system, an important part is feature extraction. A proper feature extraction method can increase the recognition ratio. In this paper, a Principal Component Analysis (PCA) based feature extraction method is investigated for developing HCR system. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. These method have been used as features of the character image, which have been later on used for training and testing with Neural Network (NN) and Support Vector Machine (SVM) classifiers. HCR is also implemented with PCA and Euclidean distance


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