1. Dataset Preparation

• Collect Data: Gather a diverse dataset of IdentityCards, Passports, Visas, and DriverLicenses.
• Label Data: Ensure each image is correctly labeled.
• Data Augmentation: Rotate, crop, and apply slight distortions to help generalization.

2. Model Selection

• Use a Convolutional Neural Network (CNN) or Transformers (Vision Transformers - ViTs) for image classification.
• Pre-trained models like ResNet, EfficientNet, MobileNet, or Vision Transformer (ViT) can be fine-tuned on your dataset.

3. Training Strategy

• Use Softmax Activation: Since you have five classes, the model will output probabilities for each.
• Use a Threshold for NotSupported: The key idea is if none of the four categories have a high confidence score (e.g., all are below 50%), classify it as "NotSupported."

4. Handling NotSupported Class Without Training Data

Since you’re not explicitly collecting "NotSupported" images:

• Use a high-quality confidence thresholding technique. If none of the class probabilities are high enough, assign "NotSupported."
• Use Out-of-Distribution (OOD) Detection techniques like:
  • Entropy-based methods: If the model is highly uncertain, classify as "NotSupported."
  • OpenMax layer: A modified softmax that helps detect unknown samples.
  • Autoencoders or One-Class SVM: Can help identify whether an image belongs to the expected distribution.

5. Model Evaluation

• Test on Real Scenarios: Ensure "NotSupported" works as expected by testing random images (e.g., landscapes, handwritten notes).
• Precision vs Recall Balance: You don’t want too many false positives (wrongly classified as a document) or false negatives (misclassifying valid documents).