Optical Character Recognition (OCR) using Deep Learning

This project implements an Optical Character Recognition (OCR) pipeline using Python, OpenCV, and Deep Learning (Keras/TensorFlow).
The notebook focuses on image preprocessing, feature extraction, and character recognition, with a specific emphasis on license plate detection and recognition.

All experiments and results are contained within a single Jupyter Notebook.

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📁 Project Structure

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├── Optical character recognition.ipynb   # Main Jupyter notebook
├── data.zip                               # Dataset (images / labels)
├── README.md                              # Project documentation

📌 Project Overview

The notebook performs the following tasks:

-Image loading and visualization

• Image preprocessing using OpenCV
• License plate detection using contour analysis
• Image segmentation and resizing
• Model training using Convolutional Neural Networks (CNN)
• Optical character recognition on detected regions

🧰 Software Requirements

Programming Language

pip install numpy pandas matplotlib opencv-python imutils tensorflow keras

It is recommended to use a virtual environment or conda environment.

Required Libraries

pip install numpy pandas matplotlib opencv-python imutils tensorflow keras

📊 Dataset

• The dataset is provided as data.zip
• Contains training images used for OCR / character recognition
• Must be extracted before running the notebook

unzip data.zip

Ensure the dataset path inside the notebook matches your local directory structure.

How to Run

1. Clone the repository:

git clone <YOUR_REPOSITORY_URL>
cd <repository_name>

2. Extract the dataset:

unzip data.zip

3. Launch Jupyter Notebook:

jupyter notebook

4. Open:

Optical character recognition.ipynb

Techniques & Tools Used 🧠

This project leverages a combination of Computer Vision and Deep Learning techniques for optical character recognition.

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🖼️ Computer Vision (OpenCV)

The following OpenCV-based techniques are used for image processing and region extraction:

• Image preprocessing
• Contour detection
• Region of Interest (ROI) extraction

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🤖 Deep Learning

Deep learning is used for character classification and recognition:

• CNN-based classification
• Keras with TensorFlow backend
• ImageDataGenerator for data augmentation

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📜 License

• This project is intended for academic and learning purposes.
• You are free to modify and extend the notebook.

👤 Author

• Shakthibala

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✅ Why this README is correct

• Matches ipynb-style projects
• Clear dataset instructions (data.zip)
• Recruiter & academic friendly
• GitHub-rendering safe
• Easy for anyone to reproduce results

If you want, I can:

• Add dataset description tables
• Add results screenshots section
• Rewrite this as a resume-grade ML project
• Convert notebook into a Python package structure

Just tell me 👍