A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits.

Journal: Computational intelligence and neuroscience
Published Date: Jul 15, 2022

Abstract

Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine Learning and Depp Learning algorithms are used in this project to measure the accuracy of handwritten displays of letters and numbers. Also, we show the classification accuracy comparison between them. The results showed that the CNN classifier achieved the highest classification accuracy of 98.83%.

Authors

  • Ayushi Sharma
    Department of Computer Science and Engineering SoE, Galgotias University, Greater Noida, India.
  • Harshit Bhardwaj
    CSE Department, Gautam Buddha University, Greater Noida, India.
  • Arpit Bhardwaj
    Department of Computer Science and Engineering, BML Munjal University, Gurugram, India.
  • Aditi Sakalle
    CSE Department, Gautam Buddha University, Greater Noida, India.
  • Divya Acharya
    HCL Technology Limited, Noida, India.
  • Wubshet Ibrahim
    Department of Mathematics, Ambo University, Ambo, Ethiopia.

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