https://github.com/uni-creator/handwritten_number_recognition_system
A Handwritten Number Recognition System built from scratch using Deep Learning from Scratch. The model is trained on digit images and can classify handwritten numbers with high accuracy.
https://github.com/uni-creator/handwritten_number_recognition_system
computer-vision deep-learning handwritten-digit-recognition image-processing implementation-from-scratch minst nueral-networks numpy pandas pytorch
Last synced: 6 months ago
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A Handwritten Number Recognition System built from scratch using Deep Learning from Scratch. The model is trained on digit images and can classify handwritten numbers with high accuracy.
- Host: GitHub
- URL: https://github.com/uni-creator/handwritten_number_recognition_system
- Owner: Uni-Creator
- License: apache-2.0
- Created: 2025-03-24T12:01:08.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-24T12:06:15.000Z (7 months ago)
- Last Synced: 2025-03-24T13:23:49.422Z (7 months ago)
- Topics: computer-vision, deep-learning, handwritten-digit-recognition, image-processing, implementation-from-scratch, minst, nueral-networks, numpy, pandas, pytorch
- Language: Python
- Homepage:
- Size: 18.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Handwritten Number Recognition System
 
## 📌 Overview
The **Handwritten Number Recognition System** is a deep learning model built from scratch to classify handwritten digits using a custom neural network. It is trained on the MNIST dataset and utilizes NumPy-based implementation for forward and backward propagation.
## 🚀 Features
- **Custom-built Neural Network**: Implemented using NumPy without deep learning frameworks like TensorFlow or PyTorch.
- **Forward & Backward Propagation**: Includes ReLU and Tanh activation functions for optimization.
- **Model Training & Evaluation**: Supports training on both CPU and GPU.
- **Manual & Automated Testing**: Test the model using predefined test images or random inputs.
- **Performance Metrics**: Displays accuracy and prediction confidence.
## 🏗️ Tech Stack
- **Python**
- **NumPy** (for matrix computations)
- **Matplotlib** (for visualization)
- **Pandas** (for data handling)
- **GPU Support** (via CUDA for optimized training)
## 📂 Project Structure
```
Handwritten_Number_Recognition_System/
│── data/ # Dataset storage
│── main.py # Loads trained model and tests data
│── model.npz # Saved model parameters
│── trainer.py # Trains the neural network model
│── trainOnGPU.py # Optimized training for GPU acceleration
│── README.md # Project documentation
```
## 📦 Installation & Setup
1. **Clone the repository**
```sh
git clone https://github.com/Uni-Creator/Handwritten_Number_Recognition_System.git
cd Handwritten_Number_Recognition_System
```
2. **Install dependencies**
```sh
pip install numpy pandas matplotlib
```
3. **Train the model (if needed)**
```sh
python trainer.py
```
4. **Run the model for testing**
```sh
python main.py
```
## 📊 How It Works
1. The model loads pre-trained weights from `model.npz` or `model.pth`.
2. A test image is provided for prediction.
3. The model outputs a digit classification with confidence score.
4. The prediction is displayed along with the corresponding test image.
## 🛠️ Future Improvements
- Implement CNN-based architecture for improved accuracy.
- Add a web interface for user-uploaded handwritten digit classification.
- Support for different datasets beyond MNIST.
## 🤝 Contributing
Contributions are welcome! Feel free to open an **issue** or submit a **pull request**.
## 📄 License
This project is licensed under the **Apache-2.0 license**.
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