HANDWRITTEN DIGIT RECOGNITION USING CNN

Project Algorithm :

CNN

Project Overview :

The issue of transcribed digit acknowledgment has for some time been an open issue in the field of example order. A few examinations have demonstrated that Neural Networks offer excellent performance in data classification. This project presents a reliable and efficient method for recognizing handwritten digits using Convolutional Neural Networks (CNN). CNNs outperform traditional neural networks due to their spatial feature extraction capabilities and improved computational efficiency. Using the MNIST dataset, which includes 70,000 grayscale images of digits (0–9), the CNN model is trained and tested to classify images with high accuracy. This is essentially a 10-class classification problem using deep learning frameworks like TensorFlow and Keras, with support from libraries such as NumPy and Pandas.

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