The capacity of computers to recognise human handwritten digits is known as handwritten digit recognition. Because handwritten numerals are imperfect and can be generated with a variety of flavours, it is a difficult work for the machine. The answer to this issue is handwritten digit recognition, which uses an image of a digit to identify the digit that is contained in the image. In this Project I had successfully implement the handwritten digit recognition using numpy,matplotlib,sklearn and obtained the desire output.
This project implements a K-Nearest Neighbors (KNN) classifier to recognize handwritten digits from the popular Digits dataset provided by the scikit-learn library. The model is trained and evaluated on the dataset, and it provides visual feedback of its predictions.
- Clone the repository:
git clone https://github.com/your-username/knn-digit-classification.git cd knn-digit-classification
Install the required packages:
bash
pip install numpy matplotlib scikit-learn
2.Usage
Run the script:
python knn_digit_classification.py
The script will load the Digits dataset, train the KNN model, and print the accuracy of the classifier. It will also display a visual representation of some sample predictions made by the model.
3.Navigate to the cloned directory:
bash
cd Code_Clause-HandWritten-Digit_Recognization
4.Create a new file named README.md and paste the above content into it. Add your Python script (let's say you name it knn_digit_classification.py) to the repository. Stage your changes:
bash
git add README.md knn_digit_classification.py
5.Commit your changes:
bash
git commit -m "Add K-Nearest Neighbors digit classification project"
6Push your changes to GitHub:
bash
git push origin main