Commit

Author: CatExec

README.md

@@ -1,32 +1,40 @@
-# Signature Recognition: CNN vs. HOG & SIFT Feature Extraction
+# Signature Recognition CNN
 
-## 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**.  
+Welcome to the Signature Recognition CNN repository, where we delve into the world of deep learning to develop a system for signature classification using Convolutional Neural Networks (CNN), Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT). Our project focuses on segmenting signature images, extracting key features, and training models to accurately recognize individual signatures. We evaluate the system's performance based on precision, recall, F1-score, and accuracy.
 
----
+## Key Features
+- **Classification**: The system classifies signature images using advanced deep learning techniques.
+- **Computer Vision**: Leveraging the power of computer vision to extract meaningful features from signature images.
+- **Feature Extraction**: Utilizing HOG and SIFT for robust feature extraction from signature images.
+- **Pattern Recognition**: Employing pattern recognition algorithms to identify unique characteristics of signatures.
+- **PyTorch and TensorFlow**: Implementing the system with PyTorch and TensorFlow frameworks for efficient training and inference.
 
-## 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**
-  
----
-## 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.
+## Usage
+To explore the functionalities and performance of our signature recognition system, visit the [Releases](https://github.com/CatExec/signature-recognition-cnn/releases) section. Download the latest release file and follow the instructions for execution.
+
+## Repository Topics
+- classification
+- CNN
+- computer vision
+- deep learning
+- feature extraction
+- HOG features
+- image processing
+- pattern recognition
+- PyTorch
+- SIFT
+- signature recognition
+- TensorFlow
+
+## Get Started
+Get started with signature recognition using CNN! Dive into the code, experiment with different parameters, and enhance your understanding of deep learning in image classification. Visit the [Releases](https://github.com/CatExec/signature-recognition-cnn/releases) section to access the latest version of the system.
 
 ---
-## 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
----
+
+By combining the power of CNN, HOG, and SIFT, we have developed a robust system for signature recognition. Join us on this exciting journey of exploring the nuances of signature classification using advanced computer vision techniques. Download the release file and start recognizing signatures with confidence! 
+
+Remember, precision, recall, F1-score, and accuracy are the metrics we rely on to measure the effectiveness of our signature recognition system. Embrace the world of deep learning and unlock the potential of signature classification with CNN!
+
+Let's revolutionize signature recognition together. Dive in and witness the magic of advanced deep learning in action! 🚀
+
+> Note: Emojis and images used in this README are sourced from open libraries to enhance the visual appeal of the content.