Instructions to use Filiphw/doctr-crop-orientation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- docTR
How to use Filiphw/doctr-crop-orientation with docTR:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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Download README.md from Filiphw/doctr-crop-orientation: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
-
https://huggingface.co/Filiphw/doctr-crop-orientation/resolve/main/README.md
- Command line
-
hf download hf://Filiphw/doctr-crop-orientation/README.md
-
curl -L -o README.md https://huggingface.co/Filiphw/doctr-crop-orientation/resolve/main/README.md
1.08 kB
metadata
language: en
tags:
- ocr
- pytorch
- doctr
- classification
Optical Character Recognition made seamless & accessible to anyone, powered by PyTorch
Task: classification
https://github.com/mindee/doctr
Example 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)