Downloads · 30 days
15K
1% of all-time downloads
doctr-torch-parseq-multilingual-v1
Felix92/doctr-torch-parseq-multilingual-v1
doctr-torch-parseq-multilingual-v1 is a machine learning model from Felix92. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
<p align="center" <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logodoctr.gif&src=0" width="60%" </p
All-time downloads
1.5M
Public
Repo size
95.7 MB
Likes
14
Public
Repo makeup
Click a slice to open those files.
.bin47.9 MB · 100%
At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Jan 4, 2024
- Updated
- Mar 8, 2024
- SHA
- 8bde31c8
Spaces
Model card
From the Hugging Face model README
<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 TensorFlow 2 & PyTorch
Task: recognition
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)
- Library
- transformers
- License
- apache-2.0
- Languages
- en, da, fr, it, es, de
- Created
- Jan 4, 2024
- Updated
- Mar 8, 2024