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CONTRIBUTING.md

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Contributing to Layout Parser

🙌 Thank you for reading this and plan to contribute! We hope you can join us and work on this exciting project that can transform document image analysis pipelines with the full power of Deep Learning.

All kinds of contributions are welcome, including but not limited to:

  • Better documentation and examples for more use cases
  • New pre-trained layout detection models
  • New features

Planned features

We are planning to improve different aspects of Layout Parser, any feedbacks and contributions would be great!

Layout Modeling

(Pre-trained) layout models are one of the most important components in Layout Parser, and we are planning to broaden the support for layout models:

  • Support framework other than Detectron2, e.g., MMOCR. It may lead to easier installation and support for more application scenarios like receipt or invoice detection.
  • Support segmentation-based models, e.g., dhSegment
  • Better customized training of layout detection models, see layout-model-training
  • Reproducing novel layout models in the current framework, e.g., CascadeTabNet

We are also working on the Layout Parser platform that can support users' sharing their own models. Please check community-platform for more detail.

Advanced Layout Pipeline

  • Support defining Pipeline that specifies an end-to-end layout processing pipeline for complex documents

Command Line Tool and Layout Detection Service

Layout Parser can be easily turned into a command line tool or service to process documents in bulk

  • Build a command line tool based on Click that supports commands like layoutparser process --path <path/to/document/folders>
  • Build a RESTful Layout Parser service based on tools like FastAPI with similar supports as the command line tool
  • Performance improvements for such services

Easy Installation and Deployment

  • Better ways for installing Detectron2 and related components on Windows machines
  • A Docker configuration for installing the Layout Parser

How to Contribute?

This how-to-guide is abridged from the MMOCR Repository.

Main Steps

  1. Fork and pull the latest Layout Parser Repository
  2. Checkout a new branch (do not use main branch for PRs)
  3. Commit your changes
  4. Create a PR

Notes:

  1. If you plan to add some new features that involve big changes, please open an issue to discuss with us first
  2. If you are the author of some papers and would like to include your method into Layout Parser, please let us know (open an issue or contact the maintainers). Your contribution would be much appreciated.
  3. For new features and new modules, unit tests are required to improve the code robustness
  4. You might want to run pip install -r dev-requirements.txt to install the dev-dependencies.

Code Style

  1. We adopt PEP8 as the preferred code style.
  2. We use the following tools for linting and formatting:
    • pylint: linter
    • black: formatter
  3. We suggest adding type hints for all APIs.

Sincere thanks,

Zejiang (Shannon) Shen