What is Signature Classification?
Signature classification is a project where we teach a computer program to identify different people's signatures. The computer learns to recognize unique features like the shape, size, and style of each signature. This technology can be helpful in verifying signatures for banks, schools, and other organizations.
By training a model with examples of signatures, it becomes smart enough to predict the owner of a new signature that it has not seen before.
How Does It Work?
This project uses a computer program called a machine learning model. Specifically, it uses a pre-built model called MobileNetV3 to analyze the features of the signature. Here’s what happens step by step:
- Training: The computer is shown many signature examples to learn their features.
- Retraining: New classes (types) of signatures can be added to the model, and it can be retrained to include these new signatures.
- Inference: The trained model is used to predict who owns a new signature by comparing its features to the ones it already knows.
Training the Model
During training, the computer learns to recognize each signature by analyzing the patterns, curves, and shapes in the images. The model is trained using many images of signatures, grouped into classes.
Each group (class) represents a person. The computer uses these groups to understand what makes one signature different from another.
Features of the Project
- Trains from scratch with a simple dataset of signature images.
- Allows adding new classes (people) and retraining the model.
- Uses advanced image processing to prepare the signatures for training.
- Can predict who owns a new signature with high accuracy.