Abstract:

A digital digit recognition system using deep learning convolutional neural networks is a technique used to recognize numbers that are not written accurately. It is an effective way to identify numbers that differ in size and shape, which may be difficult for humans to do. This system uses the MNIST dataset as its training data, which contains images of handwritten digits from 0-9. By using neural networks, this system can recognize those digits with high accuracy and speed. The accuracy of the recognized digit is represented using a graphical user interface. The DCNN architecture consists of multiple layers of interconnected nodes trained using a large dataset of labeled images. The system uses a process called convolution to analyze the features of the images and learn how to differentiate between digits

Description:

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