tashmikapillay/South-African-Bank-Notes-Recognition

This project builds a full image processing and computer vision pipeline to classify South African bank notes across five denominations: R10, R20, R50, R100, and R200, covering both old and new series notes. # 💵 South African Bank Notes Recognition This project builds a full image processing and computer vision pipeline to classify South African bank notes across five denominations: R10, R20, R50, R100, and R200, covering both old and new series notes. The pipeline follows four stages: 1. **Preprocessing and Enhancement** — grayscale conversion, CLAHE, and Gaussian blur to normalise images 2. **Segmentation** — Otsu's thresholding and Canny edge detection to isolate the note from its background 3. **Feature Extraction** — HOG, LBP, and colour histograms combined into one feature vector per image 4. **Classification** — SVM, Random Forest, and KNN trained and compared on the extracted features The system is invariant to the side photographed (front or back), scale, and rotation. --- ## 🗂️ Repository Structure ``` . ├── South African Bank Note Recognition.ipynb # Main notebook — run this ├── dataset/ # Place your image dataset here (see below) │ ├── R10/ │ ├── R20/ │ ├── R50/ │ ├── R100/ │ └── R200/ └── README.md ``` --- ## 🛠️ Requirements The notebook runs on Python 3.10 or later. Install dependencies with: ```bash pip install numpy opencv-python scikit-image scikit-learn matplotlib seaborn Pillow ipywidgets joblib ``` Or if you are on Google Colab, all of these are already available except `ipywidgets`, which Colab also includes by default. --- ## 📂 Dataset Setup The notebook expects images organised into denomination subfolders. Each subfolder should be named after its denomination exactly as shown below. **For local execution**, place the `dataset/` folder in the same directory as the notebook: ``` South African Bank Note Recognition.ipynb dataset/ R10/ ← images of R10 notes (front and back, old and new series) R20/ R50/ R100/ R200/ ``` **For Google Colab**, upload the `dataset/` folder to your Google Drive at the following path before running: ``` MyDrive/dataset/ ``` The notebook automatically detects whether it is …