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    • Integrate contributions into your pipeline
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    • Preparing your model for inference
    • Train your own model
    • AWS Lambda
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  • GitHub
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  • Live demo on Hugging Face
  • Installation
  • docTR Notebooks
  • Choosing the right model
  • Choose a ready to use dataset
  • Integrate contributions into your pipeline
  • Share your model with the community
  • Preparing your model for inference
  • Train your own model
  • AWS Lambda
  • Community Resources
  • Community Tools
  • doctr.contrib
  • doctr.datasets
  • doctr.io
  • doctr.models
  • doctr.transforms
  • doctr.utils
  • Contributor Covenant Code of Conduct
  • Contributing to docTR
  • Changelog
  • GitHub
  • PyPI
  • Live demo on Hugging Face

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  • docTR Notebooks

docTR Notebooks#

Here are some notebooks compiled for users to better leverage the library capabilities:

Notebook

Description

Colab

[Quicktour]

A presentation of the main features of docTR

https://colab.research.google.com/assets/colab-badge.svg

[Export as PDF/A]

Produce searchable PDFs from docTR results

https://colab.research.google.com/assets/colab-badge.svg

[Using standalone predictors]

Showcase how to use detection, recognition, and orientation predictors

https://colab.research.google.com/assets/colab-badge.svg

[Dealing with rotated documents]

A presentation on how to handle documents containing rotations

https://colab.research.google.com/assets/colab-badge.svg

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© Copyright 2021-2026, Mindee.

Created by Mindee · developed and maintained by t2k GmbH and the docTR community

Released under the Apache 2.0 License