Arabic-Font-Recognition

A classifier that takes an image containing a paragraph written in Arabic and classifies the paragraph into one of four fonts: Scheherazade New, Marhey, Lemonada, or IBM Plex Sans Arabic.

Instructions to Run the Code

1. Provide the Dataset:

   • Ensure you have a dataset in a zip file named data.zip.
   • Place the data.zip file in the root directory of the project.

2. Open the Jupyter Notebook:

   • Open the Predict.ipynb notebook.

3. Run All Cells:

   • In the Jupyter Notebook, select Cell from the top menu.
   • Click on Run All to execute all the cells in the notebook.

Within each directory, you will find a README explaining its contents; please view them to understand the flow.

Pipeline

1. Preprocessing

The preprocessing stage involves several steps to enhance the quality of the input images:

• Noise Removal: Salt and pepper noise is removed using median filters.
• Binarization: Images are binarized using Otsu's thresholding technique.
• Foreground/Background Correction: Any reversals between the foreground and background are fixed by examining the image borders.
• Rotation Correction: Rotations are corrected by drawing a bounding box around the text, calculating the angle of the box, and adjusting it accordingly.
• Cropping: Images are cropped to retain only the text portion.

2. Feature Selection and Extraction

For feature selection and extraction, we experimented with various methods before finalizing on the best approach:

• Initial Experiments: Gabor Features and Laws Energy Measures were tested but did not yield satisfactory results.
• Final Approach: We use a combination of SIFT (Scale-Invariant Feature Transform) and BoVW (Bag of Visual Words) features. SIFT features are extracted from each image and fed into a trained KMeans model, which assigns each feature vector to one of 200 clusters. A histogram of these assignments forms a 200-dimensional vector, which serves as the final feature vector.

3. Model Selection and Training

Various models were tested to find the best performance:

• Models Tested: Logistic regression, decision trees, random forests, XGBoost, shallow neural networks, and convolutional neural networks on raw images.
• Best Model: The best performance was achieved with an SVM (Support Vector Machine) model with a polynomial kernel, using the SIFT + BoVW features.

4. Performance Analysis

The performance of the final model is highly impressive:

• Accuracy: The model achieves an accuracy of 99.875% on the test set, correctly classifying 799 out of 800 examples.
• F1-Score: The F1-score is very close to 1.00, indicating excellent precision and recall.