An Innovative Method to Analyse the Prediction Rate and Accuracy for Handwritten Digit Recognition with Convolutional Neural Network Over Connection Temporal Classification

Abstract

Aim: Recognizing the Handwritten Digits to find the best accuracy using Machine learning methods such as Connectionist Temporal Classification (CTC) and Convolutional Neural Network (CNN). Methods and Materials: Accuracy and loss are performed with the MNIST dataset from the Keras library. The two groups Connectionist Temporal classification (N=20) and Convolutional Neural Network algorithms (N=20). Results: A CNN is used for recognizing the innovative handwritten digits. The accuracy is analysed based on correctness of the exact digits of 92.67% where the CTC has the accuracy of 89.07%. The two algorithms CNN and CTC are statistically satisfied with the independent sample T-Test (=.001) value (p<0.05) with confidence level of 95%. Conclusion: Recognizing the handwritten digits significantly seems to be better in CNN than CTC.