An Improved and Customized Hybrid of Deep and Machine Learning Technique Model for Handwritten Digit Recognition

Abstract

In modern research, pattern recognition is a vast field for the academicians and researchers to contribute their work. Various kinds of patterns like images, character, handwritten digit, etc can be recognized and classify with the help of the intelligent techniques. This research work is concentrated on the classification of handwritten digit recognition. In the world, peoples’ handwriting is different from each other’s and uses different languages, so it is necessary to develop a model that is able to recognize handwritten digits with high accuracy. Various deep and machine learning methods have been implemented by various authors and achieved satisfactory results. This research work has proposed a hybrid technique that combines deep learning and machine learning algorithms for classification of handwritten digit characters. The proposed model has two steps, first feature extraction, and second is classification of handwritten digits. The Convolutional neural network (CNN) has been used as feature extractor, and support vector machine (SVM), K-nearest neighbors (KNN), and Random Forest Classifier (RFC) algorithms have been used to recognize and classify handwritten digits. We have used very famous handwritten digit dataset as MNIST that contains 70000 samples of 0-9 digits, and EMNIST is an extended version of the MNIST dataset, that contains 280000 handwritten digit samples. The contribution of this research work is to enhance the recognition rate by our proposed hybrid models as CNN with SVM named as CNN-SVM, CNN with RFC named as CNN-RFC, and CNN with KNN named as CNN-KNN model, and achieved excellent recognition rate with both MNIST and EMNIST data samples. We have achieved the testing accuracy as 99.45% by CNN-RFC, 99.48% by CNN-KNN, and 99.55% by CNN-SVM with MNIST dataset while 99.54% by CNN-RFC, 99.46% by CNN-KNN, and 99.66% by CNN-SVM with EMNIST dataset. The testing accuracy achieved by our proposed model is tremendous and higher than previous research work.