Doctr Dummy Torch Crnn Vgg16 Bn

🚀 Doctr - Optical Character Recognition

Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch

This project provides an easy - to - use solution for optical character recognition. It allows users to perform OCR tasks with the help of models from the hub, leveraging the power of TensorFlow 2 and PyTorch.

🚀 Quick Start

The project's GitHub repository can be found at https://github.com/mindee/doctr.

✨ Features

• Seamless OCR: Makes optical character recognition easy for everyone.
• Powered by Major Frameworks: Utilizes TensorFlow 2 and PyTorch.
• Model from Hub: Allows users to load models from the hub for OCR tasks.

💻 Usage Examples

Basic Usage

>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub

>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')

>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>>                           reco_arch=model,
>>>                           pretrained=True)

>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>>                           reco_arch='crnn_mobilenet_v3_small',
>>>                           pretrained=True)

>>> # Get your predictions
>>> res = predictor(img)