Deep Learning Approach for Digit Recognition using the MNIST Dataset

Deep Learning Approach for Digit Recognition using the MNIST Dataset

Authors

  • Aishwarrya Shrivastava
  • Shashank Arya
  • Ragini Pandey

Keywords:

Deep Learning, Convolutional Neural Networks (CNNs), Digit Recognition, MNIST Dataset, Computer Vision, Image Classification, Data Preprocessing

Abstract

Digit identification has been a crucial function in computer systems with widespread implementations in different domains, for eg.  image processing & handwriting recognition. Deep machine learning methods that have been used, especially CNNs, have displayed remarkable outcomes in digit identification. In our research paper, we presented a CNN-based approach for digit recognition using the MNIST dataset, which is a typical point of reference dataset for this task. MNIST comprises Seventy Thousand grayscale images of handwritten digits of pixel size 28 x 28.

We have implemented and trained our model using the TensorFlow and Keras libraries. Our approach achieved an accuracy of 99.10% on the test set, indicating its effectiveness.

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References

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Additional Files

Published

30-05-2023

How to Cite

Aishwarrya Shrivastava, Shashank Arya, & Ragini Pandey. (2023). Deep Learning Approach for Digit Recognition using the MNIST Dataset. Vidhyayana - An International Multidisciplinary Peer-Reviewed E-Journal - ISSN 2454-8596, 8(si7), 232–239. Retrieved from https://vidhyayanaejournal.org/journal/article/view/821
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