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@charlesmindee charlesmindee released this 29 Sep 11:51
· 205 commits to main since this release
v0.6.0
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Learn about vigilant mode.

Highlights of the release:

Note: doctr 0.6.0 requires either TensorFlow >= 2.9.0 or PyTorch >= 1.8.0.

Full integration with Huggingface Hub (docTR meets Huggingface)

hf

  • Loading from hub:
from doctr.io import DocumentFile
from doctr.models import ocr_predictor, from_hub
image = DocumentFile.from_images(['data/example.jpg'])
# Load a custom detection model from huggingface hub
det_model = from_hub('Felix92/doctr-torch-db-mobilenet-v3-large')
# Load a custom recognition model from huggingface hub
reco_model = from_hub('Felix92/doctr-torch-crnn-mobilenet-v3-large-french')
# You can easily plug in this models to the OCR predictor
predictor = ocr_predictor(det_arch=det_model, reco_arch=reco_model)
result = predictor(image)
  • Pushing to the hub:
from doctr.models import recognition, login_to_hub, push_to_hf_hub
login_to_hub()
my_awesome_model = recognition.crnn_mobilenet_v3_large(pretrained=True)
push_to_hf_hub(my_awesome_model, model_name='doctr-crnn-mobilenet-v3-large-french-v1', task='recognition', arch='crnn_mobilenet_v3_large')

Documentation: https://mindee.github.io/doctr/using_doctr/sharing_models.html

Predefined datasets can be used also for recognition task

from doctr.datasets import CORD
# Crop boxes as is (can contain irregular)
train_set = CORD(train=True, download=True, recognition_task=True)
# Crop rotated boxes (always regular)
train_set = CORD(train=True, download=True, use_polygons=True, recognition_task=True)
img, target = train_set[0]

Documentation: https://mindee.github.io/doctr/using_doctr/using_datasets.html

New models (both frameworks)

  • classification: VisionTransformer (ViT)
  • recognition: Vision Transformer for Scene Text Recognition (ViTSTR)

Bug fixes recognition models

  • MASTER and SAR architectures are now operational in both frameworks (TensorFlow and PyTorch)

ONNX support (experimential)

  • All models can now be exported into ONNX format (only TF mobilenet left for 0.7.0)

NOTE: full production pipeline with ONNX / build is planned for 0.7.0 (the models can be only exported up to the logits without any post processing included)

Further features

  • our demo is now also PyTorch compatible, thanks to @odulcy-mindee
  • it is now possible to detect the language of the extracted text, thanks to @aminemindee

What's Changed

Breaking Changes 🛠

New Features

Bug Fixes

Improvements

Miscellaneous

New Contributors

Full Changelog: v0.5.1...v0.6.0

Contributors

fharper, jsn5, and 14 other contributors
Assets 18
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