Handwritten Digit Classification Using CNN
Trained a CNN model on MNIST by applying image preprocessing, convolution-pooling feature extraction, dense classification layers, and deployed a custom prediction function for classifying new handwritten digit images.
🙃I am still working on this project please visit later for detailed view
Deep Learning & Frameworks
- TensorFlow
- Keras Sequential API
Model Architecture
- Conv2D layers (32 & 64 filters)
- MaxPooling layers (2×2)
- Flatten layer
- Dense hidden layer (128 units, ReLU)
- Dense output layer (10 units, Softmax)
Data Preprocessing
- MNIST dataset loading
- Image inversion (255 - pixel values
- Normalization (0–1)
- Channel reshaping (28×28×1)
- One-hot encoding of labels
Training Configuration
- Optimizer: Adam
- Loss: Categorical Crossentropy
- Epochs: 10
- Batch size: 200
- Validation split: 20%
Deployment & Inference
- Model saving:
mnist_cnn.h5 - Custom prediction pipeline using Pillow, OpenCV, NumPy
- On-image confidence score visualization with Matplotlib