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Handwritten digit and letter recognition using hybrid dwt-dct with knn and svm classifier

Handwritten digit and letter recognition is one of the oldest and a very important topic in the field of pattern recognition. Handwritten digit and letter recognition poses different problem because of different writing styles, similarity in structure and angle of orientation. Therefore it is very important to find effective method for recognition and classification of digit and letter. Handwritten digit and letter recognition has various applications such as number plate recognition, extracting business card information, bank check processing, postal address processing, passport processing, signature processing etc. This paper propose a method of handwritten digit and letter recognition using feature extraction based on hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). These extracted features are passed to K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers for classification. Standard MNIST and EMNIST letter dataset are used for this experiment. Firstly MNIST digit and EMNSIT letter dataset are binarized and later stray pixels are removed. Features are extracted using hybrid Discrete Wavelet Transform and Discrete Cosine Transform. KNN and SVM classifiers are used for classification purpose. The proposed method was able to obtain a highest accuracy of 97.74% for digit and 89.51% for letter using SVM classifier.

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