Our Handwritten Digit Recognition System employs Neural Networks to accurately identify and classify handwritten digits.
The Handwritten Digit Recognition System is a project that uses Neural Network technology to identify and classify scanned images of handwritten digits. This project is based on the MNIST dataset and demonstrates advanced pattern recognition techniques.
- Accurate detection of scanned handwritten digits.
- Neural Network architecture for efficient pattern recognition.
- Built using the MNIST dataset, a well-known benchmark in the field.
- Easy-to-use interface for digit recognition.
Follow these instructions to get the project up and running on your local machine:
- Navigate to the project directory:
cd handwritten-digit-recognition - Install dependencies:
pip install -r requirements.txt - Run the system:
python main.py
- Launch the system.
- Upload or input scanned images containing handwritten digits.
- The Neural Network will process the images and display the recognized digit(s).
The project utilizes the MNIST dataset, a benchmark dataset in machine learning and computer vision. It consists of a large collection of handwritten digits along with corresponding labels.
At the core of this project is a Neural Network model that has been trained on the MNIST dataset. The network is capable of learning complex patterns and features, resulting in accurate digit recognition.
Contributions to the Handwritten Digit Recognition System are highly appreciated. If you encounter any issues or have ideas for improvements, please feel free to open an issue or pull request.
By utilizing Neural Network technology and the MNIST dataset, the Handwritten Digit Recognition System demonstrates the potential of pattern recognition in identifying scanned images of handwritten digits. Explore, experiment, and contribute to this project