OCR Model Training and Prediction

This project is designed to train and use an Optical Character Recognition (OCR) model for recognizing characters in CAPTCHA images.

Project Structure

• mb_capcha_ocr/: Contains the core OCR model and prediction logic.
• train_model/: Contains the training script for the OCR model.

Installation and Setup for Training

1. Clone the repository:

   git clone https://github.com/thedtvn/mbbank-capcha-ocr
   cd mbbank-capcha-ocr

2. Create and activate a virtual environment:

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

3. Install the required dependencies:

   pip install -r requirements.txt

Training the Model

1. Place your training and testing images in the dataset/ directory. The images should be named in the format {label}.(png|jpg|jpeg).

2. Run the training script:

   python train_model/train.py

3. The trained model will be saved as model.pt in the root directory.

Using the Model for Prediction

1. Import the predict function from the mb_capcha_ocr module:

   from mb_capcha_ocr.predict import predict

2. Use the predict function to get the predicted text from an image:

   from PIL import Image
   
   img = Image.open("path_to_image.png")
   predicted_text = predict(img)
   print(predicted_text)

Files

• train_model/train.py: Script to train the OCR model.
• mb_capcha_ocr/predict.py: Script to predict text from an image using the trained OCR model.
• requirements.txt: List of dependencies required for the project.

Dependencies

• Python 3.x
• torch
• torchvision
• matplotlib
• Pillow

License

This project is licensed under the MIT License. See the LICENSE file for more details.