This project develops a deep learning model for Optical Character Recognition (OCR) to identify Tamil characters from printed texts. It leverages a custom-built convolutional neural network (CNN) to process images and recognize characters, addressing the unique challenges posed by the Tamil script's complexity.

• Custom CNN Architecture: Tailored network to handle the intricacies of Tamil characters.
• Reduced Character Classes: Simplifies the recognition process by reducing the classes to 108.
• Preprocessing and Training: Detailed steps for image preprocessing, model training, and evaluation.
• Real-Time OCR Capability: Designed to handle real-time image processing and character recognition.
• Image Preprocessing: Automates the conversion of images to grayscale, resizes them, and extracts labels for training.
• Robust CNN Architecture: Employs Conv2D, MaxPooling2D, Flatten, and Dense layers to effectively recognize Tamil characters.
• Training and Evaluation: Includes scripts for training the model on a curated dataset and evaluating its accuracy on a separate test set.

Ensure the following are installed:

• Python 3.x
• TensorFlow
• Keras
• NumPy
• Matplotlib
• OpenCV

1. Clone the repository.
2. Install dependencies.
3. Run the script tamil_ocr.py to train the model and perform OCR on new images.

Contributions are welcome! Please feel free to submit pull requests, report issues, or suggest improvements.