Author Login IJIRT Partner Network (IPN) Login

Fundamental Libraries to Train Handwritten Digit Recognition Models

  • Unique Paper ID: 169477
  • Volume: 11
  • Issue: 6
  • PageNo: 1091-1096
  • Abstract:
  • This paper provides a comprehensive overview of training and testing methodologies for handwritten digit recognition using a combination of machine learning and deep learning tools. The study leverages popular datasets such as MNIST and EMNIST to evaluate different model architectures, ranging from traditional machine learning algorithms like Support Vector Machines (SVM) and k-Nearest Neighbors (KNN) to advanced deep learning models such as Convolutional Neural Networks (CNNs). We outline the steps involved in data preprocessing, model building, and evaluation using industry-standard frameworks like TensorFlow, Keras, and PyTorch. The training process focuses on teaching models to recognize digit patterns, while the testing phase measures their generalization abilities on unseen data using metrics such as accuracy, precision, recall, and F1 score. Additionally, the paper discusses deployment strategies for integrating these models into real-world applications using tools like Flask and TensorFlow Lite, ensuring robust and scalable solutions. Our findings provide a structured approach to developing effective handwritten digit recognition systems using state-of-the-art machine learning and deep learning methodologies.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 6
  • PageNo: 1091-1096

Fundamental Libraries to Train Handwritten Digit Recognition Models

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry