This project implements a Convolutional Neural Network (CNN) for classifying handwritten digits using TensorFlow.js. The web app allows users to interact with the model and classify handwritten digits in real-time.
The CNN architecture consists of:
- Two convolutional layers for feature extraction
- Max-pooling layers for downsampling
- ReLU activation for introducing non-linearity
- Variance scaling for normalization
- A flattened layer to prepare the data for the final layer
- A dense layer with 10 neurons and softmax activation for predicting class probabilities (0-9)
The model is trained using the Adam optimizer and categorical cross-entropy loss function.
- Backend: TensorFlow.js
- Frontend: HTML, CSS, Bootstrap
You can interact with the web app and classify handwritten digits here: https://rrrinav.github.io/MNIST-Digit-Classifier/