This is a Handwritten Digit Recognition Model is a deep learning artificial intelligence model that can recognize handwritten digits with a high degree of accuracy. The model has been built using the TensorFlow and Keras frameworks and is trained on a large dataset of handwritten digit images called MNIST. With an accuracy of over 98%, this model has the potential to be used in a variety of applications, from automated form processing to recognizing handwritten numbers on checks and other financial documents. The model is easy to use and can be integrated into other software systems for automated digit recognition.
The MNIST dataset is a large collection of handwritten digits that is commonly used as a benchmark dataset for testing image recognition algorithms. It consists of 60,000 training images and 10,000 testing images, each of which is a grayscale image of a handwritten digit. The images are normalized and centered to make them easier to process, and the dataset also includes corresponding labels that indicate the correct digit for each image. MNIST is widely used in the machine learning community for testing and benchmarking image recognition models, and has been used in numerous research papers and competitions.
Python 3.x
TensorFlow 2.x
Keras
NumPy
Pandas
Matplotlib
scikit-learn