Handwriting-Predictive-A.I

A FastAPI-based RESTful API that identifies a person's handwriting using a trained PyTorch ResNet18 model. Upload a handwriting image and get the predicted writer with confidence scores. This project is aimed at combating exam malpractice by verifying handwriting.

🚀 Tech Stack

• Backend: FastAPI
• Machine Learning: PyTorch, Torchvision
• Image Processing: Pillow
• Server: Uvicorn

📂 Project Structure

.
├── app/
│   ├── main.py         # FastAPI application, endpoints
│   ├── model.py        # Model loading and prediction logic
│   └── utils.py        # Image preprocessing utilities
├── data/
│   └── handwriting_dataset_training/ # Training images
├── models/
│   └── handwriting_model.pt # Trained PyTorch model
├── .gitignore
├── README.md
├── requirements.txt    # Project dependencies
└── train.py            # Script to train the model

⚙️ Installation and Setup

1. Clone the repository:

   git clone https://github.com/your-username/Handwriting-Predictive-A.I.git
   cd Handwriting-Predictive-A.I

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

USAGE

1. Run the FastAPI server:

   uvicorn app.main:app --reload

   The application will be available at http://127.0.0.1:8000.

2. Use the prediction endpoint: You can use tools like curl or the interactive API documentation at http://127.0.0.1:8000/docs to send a POST request to the /predict endpoint with an image file.

   Example using curl:

   curl -X POST -F "file=@/path/to/your/image.jpg" http://127.0.0.1:8000/predict

   Sample Prediction Response:

   {
     "label": "Dubem_test",
     "confidence": 0.9873,
     "message": "This handwriting definitely belongs to Dubem_test."
   }

🧠 Training the Model

To train the model on your own dataset:

1. Prepare your dataset: Organize your training images in the data/handwriting_dataset_training directory, with each subdirectory named after a class (e.g., data/handwriting_dataset_training/new_person).

2. Run the training script:

   python train.py

   The script will train the model and save the updated handwriting_model.pt file in the models/ directory. You can configure the training parameters (epochs, learning rate, etc.) at the top of the train.py file.