A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

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.