Welcome to the CNN-Based_Handwritten_Digit_Recognition repository! This project focuses on classifying handwritten digits using a Convolutional Neural Network (CNN) model. The main objective was to accurately classify digits ranging from 0 to 9 using the well-known MNIST dataset.
In this project, we utilized the power of CNNs to recognize and classify handwritten digits. The MNIST dataset consists of 28x28 pixel grayscale images of handwritten digits, making it a perfect candidate for image classification tasks.
- Implementation of CNN for handwritten digit recognition.
- Usage of the widely-used MNIST dataset.
- Utilization of popular libraries such as Keras and TensorFlow.
- Ability to classify digits from 0 to 9 accurately.
To get started with this project, you can download the necessary software by clicking the button below:
(Note: The link provided above needs to be launched for software download.)
- Clone the repository to your local machine.
- Install the required dependencies by running
pip install -r requirements.txt. - Run the main script to train the CNN model on the MNIST dataset.
- Test the model's accuracy on handwritten digit images.
- Make predictions on custom handwritten digits to observe the model's performance.
Contributions to this project are welcome! Here are a few ways you can contribute:
- Implement new features to enhance digit recognition.
- Optimize the current CNN model for better accuracy.
- Provide insights into improving the training process.
This project is licensed under the MIT License - see the LICENSE file for details.
If the provided link does not work or additional releases are available, please check the "Releases" section of this repository for the latest updates.
You can visit the official website for more information about the project.
Thank you for visiting the CNN-Based_Handwritten_Digit_Recognition repository! 🤖📊🔍