📝 Gujarati Handwriting Recognition

This project was developed as the final year diploma project in Computer Engineering. It involves building a machine learning pipeline to recognize handwritten Gujarati digits or characters using Convolutional Neural Networks (CNN) and a basic Neural Network (NN). The project covers the full pipeline: data preprocessing, augmentation, model training, and evaluation.

🔍 Objective

To build a robust image classification model that can recognize Gujarati handwritten characters or digits with high accuracy using custom image preprocessing and CNN/NN architectures.

⚙️ Technologies Used

• Python - for model building and preprocessing
• Pillow (PIL) - image loading and manipulation
• NumPy - array operations
• TensorFlow - for building and training models
• Goggle Colab - model training & analysis

🧹 Preprocessing Pipeline

1. Balancing Classes (get_no_of_imgs.py)

   • Counts the number of images in each class
   • Calculates how many more images are needed per class to balance the dataset

2. Resizing & Grayscale Conversion (resize_imgs.py)

   • Resizes all images to 50x70 pixels
   • Converts to grayscale
   • Organizes into class-wise directories

3. Thresholding & Binarization (absolute_0-1_filtering.py)

   • Converts images to grayscale
   • Applies a binary threshold (e.g., pixels > 130 → white, else black)
   • Saves cleaned images for further use

4. Augmentation (augment.py)

   • Applies slight rotations to existing samples
   • Saves them as new images to increase data diversity

🔃 Preprocessing results

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🧠 Model Architectures

1. CNN Model

   • Input shape: (50, 70, 1)
   • Multiple convolutional + max pooling layers
   • Dense layers for classification
   • Dropout regularization

2. Simple Neural Network (NN)

   • Input layer flattened from image
   • Dense layers with ReLU
   • Softmax output

📊 Results

• Training Accuracy: ~95% (CNN), ~80% (NN)
• Test Accuracy: ~90% (CNN), ~75% (NN)
• CNN outperformed NN significantly due to its capability to extract spatial features.