https://github.com/imamaaa/signature-recognition-cnn

A deep learning-based system for signature classification using CNN, HOG, and SIFT. This project segments signature images, applies feature extraction, and trains models to recognize individual signatures. Performance is evaluated using precision, recall, F1-score, and accuracy.
https://github.com/imamaaa/signature-recognition-cnn

classification cnn computer-vision deep-learning feature-extraction hog-features image-processing pattern-recognition pytorch sift signature-recognition tensorflow

Last synced: 2 months ago
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A deep learning-based system for signature classification using CNN, HOG, and SIFT. This project segments signature images, applies feature extraction, and trains models to recognize individual signatures. Performance is evaluated using precision, recall, F1-score, and accuracy.

• Host: GitHub
• URL: https://github.com/imamaaa/signature-recognition-cnn
• Owner: imamaaa
• Created: 2025-03-20T23:00:43.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2025-03-20T23:10:43.000Z (2 months ago)
• Last Synced: 2025-03-21T00:19:53.020Z (2 months ago)
• Topics: classification, cnn, computer-vision, deep-learning, feature-extraction, hog-features, image-processing, pattern-recognition, pytorch, sift, signature-recognition, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 3.94 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Signature Recognition: CNN vs. HOG & SIFT Feature Extraction

## Overview  

This project implements **Signature Recognition** using **Convolutional Neural Networks (CNNs)** and **manual feature extraction techniques (HOG, SIFT)**. The goal is to **classify signatures** based on different individuals and compare **CNN-based feature extraction vs. traditional techniques**.  

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## Key Objectives  

- **Segment signatures** into separate folders per individual  

- **Perform train-test split** for model evaluation  

- **Train CNN for signature classification**  

- **Compare CNN features with manual feature extraction (HOG and SIFT)**  

- **Evaluate models using Precision, Recall, F1-score, and Accuracy**  

- **Analyze performance through error plots & visualizations**

  

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## Repository Contents  

- `i201819_B_A1_Q1.ipynb` → Jupyter Notebook containing segmentation, feature extraction, and model training

- `i201819_ImamaAmjad_Ass1.pdf` → Detailed analysis, methodology, and results

- `README.md` → Project documentation (to be expanded)

  

For now, please refer to the i201819_ImamaAmjad_Ass1.pdf for dataset details, preprocessing steps, and model evaluation. The README will be expanded soon with additional explanations.

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## Future Enhancements  

- Add dataset details & preprocessing steps

- Upload sample outputs & model performance comparisons

- Expand CNN hyperparameter tuning & architecture variations

- Implement additional feature extraction techniques

- Expand the README with dataset details, preprocessing, and architecture explanations

- Add challenges faced and key lessons learned section

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