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

Ayushi Sharma1, Harshit Bhardwaj1, Arpit Bhardwaj2

  • 1Department of Computer Science and Engineering SoE, Galgotias University, Greater Noida, India.

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This summary is machine-generated.

This study compares Machine Learning and Deep Learning algorithms for handwritten digit recognition using optical character recognition (OCR). Convolutional Neural Networks (CNNs) achieved the highest accuracy at 98.83%.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Optical Character Recognition (OCR) is a key technology for digitizing documents.
  • Handwritten digit recognition is a challenging subfield within OCR.
  • The Modified National Institute of Standards and Technology (NIST) database provides valuable datasets for this task.

Purpose of the Study:

  • To evaluate and compare the performance of Machine Learning and Deep Learning algorithms for handwritten digit recognition.
  • To determine the classification accuracy of different algorithms on unique handwritten digits.
  • To identify the most effective algorithm for this specific OCR task.

Main Methods:

  • Utilized unique handwritten digits from the NIST database.
  • Implemented and trained Machine Learning algorithms.
  • Implemented and trained Deep Learning algorithms, specifically Convolutional Neural Networks (CNNs).
  • Compared the classification accuracy of the implemented algorithms.

Main Results:

  • The Convolutional Neural Network (CNN) classifier demonstrated superior performance.
  • CNN achieved the highest classification accuracy of 98.83% in recognizing handwritten digits.
  • Comparative analysis highlighted the effectiveness of deep learning approaches over traditional machine learning for this task.

Conclusions:

  • Deep learning, particularly CNNs, offers state-of-the-art performance for handwritten digit recognition.
  • The study validates the efficacy of CNNs for optical character recognition tasks.
  • Accurate handwritten digit recognition is crucial for various applications, including document analysis and data entry.