Deep Signature Recognition with CNN

Welcome to the "signature-recognition-cnn" repository, where we delve into the world of signature classification using Convolutional Neural Networks (CNN), Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT). This project focuses on segmenting signature images, applying feature extraction techniques, and training models to identify individual signatures with precision and accuracy. Let's explore the details of this deep learning-based system.

Repository Overview

Description

This project aims to develop a robust system for signature recognition by leveraging the power of deep learning methodologies. By combining CNN, HOG, and SIFT approaches, we enhance the accuracy and efficiency of recognizing signatures from images.

Topics

• Classification
• CNN
• Computer Vision
• Deep Learning
• Feature Extraction
• HOG Features
• Image Processing
• Pattern Recognition
• PyTorch
• SIFT
• Signature Recognition
• TensorFlow

System Architecture

The system follows a structured approach:

1. Image Preprocessing: Segmentation of signature images to extract relevant features.
2. Feature Extraction: Utilizing HOG and SIFT for extracting distinctive features.
3. Model Training: Training CNN models to recognize and classify signatures.
4. Performance Evaluation: Assessing model performance using precision, recall, F1-score, and accuracy metrics.

Performance Evaluation

The performance of the signature recognition system is evaluated through various metrics, ensuring the reliability and effectiveness of the models in real-world scenarios. By focusing on precision, recall, F1-score, and overall accuracy, we strive to achieve optimal results in signature classification.

Link to Releases

For accessing the executable file related to this project, please click the button above to download and execute the necessary files.

Conclusion

In conclusion, the "signature-recognition-cnn" repository offers a comprehensive exploration of signature classification using deep learning techniques. By implementing CNN, HOG, and SIFT methodologies, we aim to enhance the accuracy and efficiency of recognizing signatures from images. Feel free to visit the provided link to explore the project further.

Let's continue to innovate and explore the fascinating world of signature recognition through the lens of deep learning!