https://github.com/shivam6862/digit-recognizer
This is a digit recognizer app that utilizes artificial neural networks (ANN) and convolutional neural networks (CNN) for recognizing hand-written digits. The app is built with Next.js for the frontend, Flask for the backend, and includes components for training and testing the models.Digit Recognizer
https://github.com/shivam6862/digit-recognizer
deep-learning flask nextjs python
Last synced: 3 months ago
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This is a digit recognizer app that utilizes artificial neural networks (ANN) and convolutional neural networks (CNN) for recognizing hand-written digits. The app is built with Next.js for the frontend, Flask for the backend, and includes components for training and testing the models.Digit Recognizer
- Host: GitHub
- URL: https://github.com/shivam6862/digit-recognizer
- Owner: shivam6862
- Created: 2023-11-28T12:35:03.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-02T13:16:02.000Z (over 2 years ago)
- Last Synced: 2025-01-29T15:50:18.084Z (over 1 year ago)
- Topics: deep-learning, flask, nextjs, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.22 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Digit Recognizer App
## Overview
This is a digit recognizer app that utilizes artificial neural networks (ANN) and convolutional neural networks (CNN) for recognizing hand-written digits. The app is built with Next.js for the frontend, Flask for the backend, and includes components for training and testing the models.
## Features
- Handwritten digit recognition using ANN and CNN
- User-friendly web interface built with Next.js
- Backend server powered by Flask
- Model training and testing functionality
## Technologies Used
- Next.js
- Flask
- Python
- TensorFlow (for ANN and CNN models)
## Setup
1. Clone the repository:
```bash
git clone https://github.com/shivam6862/Digit-Recognizer.git
```
2. Install dependencies:
```bash
cd digit-recognizer/frontend
npm install
```
```bash
cd digit-recognizer/backend
pip install -r requirements.txt
```
3. Run the development server:
```bash
npm run dev
```
```bash
cd backend
python main.py
```
The app will be accessible at `http://localhost:3000`.
## Usage
1. Navigate to the web interface in your browser.
2. Upload the image in my app.
3. Click the "Recognize" button to see the model's prediction.
4. Explore the training section to train and test new models.
## Training Models
To train new models, follow these steps:
1. Prepare a dataset of hand-written digits.
2. Place the dataset in the `data` directory.
## Contributing
If you'd like to contribute to this project, please follow the guidelines in [CONTRIBUTING.md](CONTRIBUTING.md).
## License
This project is licensed under the [MIT License](LICENSE).
## Acknowledgments
- Special thanks to shivam6862 for contributing to the project.