Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit

Authors

  • Kavita Bhosle Computer Science and Engineering Department, Maharashtra Institute of Technology, India https://orcid.org/0000-0002-1261-0477
  • Vijaya Musande Jawaharlal Nehru Engineering College, MGM University, India

DOI:

https://doi.org/10.47852/bonviewAIA3202441

Keywords:

deep learning, convolutional neural network, feed-forward neural network, random forest classifier

Abstract

Devanagari character and digit recognition are a difficult undertaking because writing style depends on a person’s traits and differs from person to person. We get more precise results in digit recognition, thanks to deep learning convolutional neural networks (CNNs), which function similarly to the human brain. In this study, the CNN method was put into practice and contrasted with the feed-forward neural network and random forest approaches. In comparison to previous methods, CNN has reportedly provided an accuracy rating of up to 99.2%. CNN is effective with both organized and unstructured data, including pictures, video, and audio.

 

Received: 30 September 2022 | Revised: 21 February 2023 | Accepted: 22 February 2023

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

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Published

2023-02-22

How to Cite

Bhosle, K., & Musande, V. (2023). Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit. Artificial Intelligence and Applications, 1(2), 114–118. https://doi.org/10.47852/bonviewAIA3202441

Issue

Section

Research Article