| language: en | |
| tags: | |
| - ocr | |
| - pytorch | |
| - doctr | |
| - table_structure | |
| <p align="center"> | |
| <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> | |
| </p> | |
| **Optical Character Recognition made seamless & accessible to anyone, powered by PyTorch** | |
| ## Task: table_structure | |
| https://github.com/mindee/doctr | |
| ### Example usage: | |
| ```python | |
| >>> 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) | |
| ``` | |