A Python-based Computer Vision application designed to detect, extract, and classify alphanumeric characters in real-time. Using webcam integration and machine learning, the script processes live video feeds to identify characters held up to the camera.
Here is a look at the model processing a live video feed and outputting the predicted classifications.
- Real-Time Video Processing: Captures and processes live frames using OpenCV.
- Region of Interest (ROI) Extraction: Dynamically draws bounding boxes around detected objects/characters to isolate them from the background.
- Image Preprocessing: Converts live feeds to grayscale and applies thresholding to optimize the image for the classification model.
- Alphanumeric Classification: Passes the processed image through a trained machine learning model to predict the character (e.g., Outputting
['2']).
A lightweight, script-focused architecture for rapid execution:
📦 cv-alphabet-recognition
┣ 📜 alphabet_detection.py # Main execution script handling the webcam and ML predictions
┗ 📜 README.md # Project documentation
To test the computer vision model on your own machine, you will need a functional webcam and Python installed.
1. Clone the repository
git clone https://github.com/mohdayandev/cv-alphabet-recognition.git
cd cv-alphabet-recognition2. Install dependencies Ensure you have the required computer vision and numerical libraries installed (e.g., OpenCV, NumPy):
pip install opencv-python numpy(Note: If your script requires specific ML libraries like scikit-learn or tensorflow to load the model, install those as well).
3. Start the camera feed
python alphabet_detection.pyA window will open displaying your webcam feed. Hold a character up to the camera to see the bounding box and check your terminal for the predicted class.
Mohammed Ayan Transforming raw data and pixels into intelligent, scalable digital solutions.