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@article{169477,
        author = {Lokesh Rathore and Dr. Ramji Yadav},
        title = {Fundamental Libraries to Train Handwritten Digit Recognition Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1091-1096},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169477},
        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.},
        keywords = {Python, Jupyter Notebook, Tensorflow, Keras, OpenCV, Scikit-learn, Pytorch, Pycharm, MNIST dataset.},
        month = {November},
        }