Maximising the accuracy of handwritten alphabet recognition using Bayesian regression over random forest

This research paper deals with maximising the accuracy of recognising Handwritten Alphabet using Bayesian Recognition over Random Forest. The dataset named A-Z Handwritten Alphabets consists of 370,000 images were collected from the Kaggle repository. The suggested ML classifier model, namely Bayesian Regression and Random Forest is used in this phase. Nearly 10 iterative values from each group were taken for statistical analysis. For SPSS calculation done using G power by presetting value of 0.95 is used. The Bayesian Regression, which has an accuracy of 92.52%, outperforms the Random Forest technique, which has an accuracy of 83.42%, according to the data. Thus, it demonstrates that the Novel Bayesian Regression and Random Forest differ statistically significantly with p=0.004 (T test on independent sample p