Horizontal feature extraction of handwritten character by statistical and structural sorts compared with diagonal direction.

In this study, we take a different approach to feature extraction by focusing on the diagonal rather than the horizontal in order to statistically and structurally utilise handwritten characters. Substances and Techniques: This paper employs the Computer Vision Laboratory's (CVL) publicly available database for its dataset (NTHU). Using a G intensity of 0.8 and alpha and beta quality values of 0.05., 0.2, and a 95% confidence interval, we determined that a sample size of 280 (group 1=140, group 2=140) handwritten characters would provide adequate statistical power. Novel sample number (N=10) diagonal feature extraction and sample number (N=10) horizontal feature extraction are used to classify statistical and structural aspects of handwritten characters, resulting in greater precision. The outcomes show that the accuracy of the diagonal feature extraction classifier is 95.883, while that of the horizontal feature extraction classifier is 92.736. This study has a significance level of p=0.0213. In conclusion, statistical and structural features of handwritten characters make diagonal feature extraction more accurate than horizontal feature extraction. [ABSTRACT FROM AUTHOR]