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2025 | OriginalPaper | Chapter

Analysis of Random Forest Compared to Ridge Linear Classification for Handwritten Alphabet Recognition

Authors : J. Prakash, P. Dass

Published in: Innovative Computing and Communications

Publisher: Springer Nature Singapore

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Abstract

Aim: The suggested goal consists of analyze handwriting alphabet recognition employing Ridge Linear classification as opposed to Random Forest. Materials and Methods: The chosen dataset, which is divided into 80 and 20%, is used for the analysis. Twenty % of the data is used for testing procedures, while 80% is used for training. Two different models like Ridge Linear classifier and Random Forest were developed this time phase, and experimental analysis is carried out. To compute SPSS, a G power of 0.95 is utilized. Result: The mean accuracy of the Ridge Linear classification method, which is the recommended approach, is 93%, which is greater than the 87.42% of the conventional method. The obtained results show that there is no statistical significance difference between the Novel Ridge Linear and Random Forest with p = 0.144 (test on an independent sample, 0.05). Conclusion: The proposed algorithm performed better than the conventional method of Handwritten Alphabet Recognition.

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Title: Analysis of Random Forest Compared to Ridge Linear Classification for Handwritten Alphabet Recognition
Authors: J. Prakash
P. Dass
Publisher: Springer Nature Singapore
Book: Innovative Computing and Communications
Print ISBN: 978-981-9741-51-9

Electronic ISBN: 978-981-9741-52-6

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-981-97-4152-6_46

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