Comparison of SVM Algorithms with Decision Trees for Accurate Recognition to Handwritten Digits to Improve the Accuracy Value

Abstract

Aim: The aim of this research is to create the most efficient and accurate handwritten digit recognition system using two machine learning algorithms Support Vector Machine and Decision Tree. Materials and Methods: The datasets are extracted from the SKLEARN module using anaconda prompt and jupyter which has around 70000 sample points to solve the problem. Support Vector Machine predicts the output for dependent variable and independent variable. Sample count for group 1 Support Vector Machine is 10 and sample count for group 2 Decision Tree is 10. Total sample size is 20 for both groups using Gpower as 80%. Results: Support vector machine comes up with the mean accuracy of when contrasted with the Decision Tree algorithm. Ultimately the Support Vector Machine pops up with a better significant value than the Decision Tree algorithm. The two algorithms SVM and DT are statistically satisfied with the independent sample T-Test value (p<0.001) with confidence level of 95%. Conclusion: Within the limits of the study the Support Vector Machine has better significant accuracy value than Decision Tree algorithm. Image Processing definitely happened to convert the handwritten digit into digital image of 8 * 8 pixel size. In the Image Processing, Onces the Image is converted into digital format then the digital format image is detected using Image Detection.