The SigSafe project aims to develop a robust system capable of authenticating handwritten signatures in three distinct scripts: Bengali, Hindi, and Cedar. Utilizing Siamese Networks, a specialized type of neural network for one-shot learning tasks, the system learns to differentiate between genuine and forged signatures.
Multilingual Support: The system supports signature verification for three diverse scripts: Bengali, Hindi, and Cedar, catering to a wide range of users.
Siamese Network Architecture: Siamese Networks are employed to compare pairs of signatures and determine their similarity. This architecture is well-suited for one-shot learning tasks, making it ideal for signature verification.
Data Preprocessing: Handwritten signature datasets in Bengali, Hindi, and Cedar scripts are preprocessed to enhance readability and remove noise. Techniques such as image augmentation and normalization are employed to improve model generalization.
Training Pipeline: The Siamese Network is trained on a large dataset of genuine and forged signature pairs. During training, the network learns to discriminate between authentic and counterfeit signatures, optimizing its parameters via backpropagation.
Evaluation Metrics: Performance metrics such as accuracy, precision, recall, and F1-score are utilized to assess the model's effectiveness in distinguishing between genuine and forged signatures.
Data Collection: Obtain datasets containing genuine and forged signatures in Bengali, Hindi, and Cedar scripts.
Preprocessing: Clean and preprocess the signature images, ensuring uniformity and removing noise.
Model Training: Train the Siamese Network using the preprocessed dataset. Adjust hyperparameters as necessary to optimize performance.
Evaluation: Evaluate the trained model's performance using a separate validation dataset. Calculate relevant metrics to assess its accuracy and effectiveness.
Deployment: Deploy the trained model into a production environment for real-time signature verification applications. Integrate the system with existing authentication systems or develop a standalone application for end-users.
Python programming language Deep learning libraries such as TensorFlow or PyTorch Image processing libraries like OpenCV Signature datasets in Bengali, Hindi, and Cedar scripts Hardware resources for training and inference (GPU recommended for faster processing)
Madhumitha Kolkar
MIT License