CNN-Based Image Classification for Handwritten Digit Recognition

Authors

  • Kishan Kushwaha Department of Computer Science and Information Technology, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, AP, India
  • Sakala Rahul Department of Computer Science and Information Technology, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, AP, India
  • Shaik Eliyaz Department of Computer Science and Information Technology, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, AP, India
  • Chaitanya Reddy Department of Computer Science and Information Technology, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, AP, India
  • Amarendra K Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, AP, India

Keywords:

Handwritten digit recognition, CNN, MNIST dataset, image classification

Abstract

In the digital era, handwritten digit recognition (HDR) plays a pivotal role in converting analog information into a digital format. Traditional methods of digitizing handwritten content often come with substantial costs. This essay addresses the issue at hand by putting forth a very effective algorithm designed to accurately recognize handwritten digits from scanned images, thereby significantly reducing expenses. The study focuses on investigating and comparing the way different algorithms perform when categorizing handwritten numbers. The comparison is predicated on varying the number of hidden layers, using various epoch counts, and assessing accuracy. For the experiment, the popular Modified National Institute MNIST (Measurement, Technology, and Standards) dataset for assessment. The results of this study offer insightful information on improving HDR techniques to make handwritten information digitization easier and more affordable. In this study, a systematic exploration of HDR algorithms was conducted, varying key factors such as hidden layers and epochs. The algorithms accuracy in classifying handwritten digits from scanned images was thoroughly evaluated. Leveraging the Comprehensive Modified Nation Institute of Standards and Technology (MNIST) dataset, the research results offer detailed comparative analyses, revealing optimal configurations for HDR algorithms. These findings pave the way for significant advancements in the field, enabling industries reliant on digital conversion to adopt cost-effective, accurate, and efficient HDR methods for processing handwritten information.

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Published

02.02.2024

How to Cite

Kushwaha, K. ., Rahul, S., Eliyaz, S. ., Reddy, C. ., & K, A. . (2024). CNN-Based Image Classification for Handwritten Digit Recognition. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 91–97. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4640

Issue

Vol. 12 No. 14s (2024)

Section

Research Article

License

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