axa-group/Parsr

Transforms PDF, Documents and Images into Enriched Structured Data

JavaScript

6,169

1,872 commits

updated Mar 20, 2026

See the code

README


Turn your documents into data!

Français | Portuguese | Spanish | 中文

[!WARNING] This project is no longer maintained. Security patches are not being applied. Consider using an alternative such as LiteParse if you need a local, Apache 2.0–licensed parsing solution.

  • Parsr, is a minimal-footprint document (image, pdf, docx, eml) cleaning, parsing and extraction toolchain which generates readily available, organized and usable data in JSON, Markdown (MD), CSV/Pandas DF or TXT formats.

  • It provides analysts, data scientists and developers with clean structured and label-enriched information set for ready-to-use applications ranging from data entry and document analysts automation, archival, and many others.

  • Currently, Parsr can perform: document cleaning, hierarchy regeneration (words, lines, paragraphs), detection of headings, tables, lists, table of contents, page numbers, headers/footers, links, and others. Check out all the features.

Table of Contents

Getting Started

Installation

-- The advanced installation guide is available here --

The quickest way to install and run the Parsr API is through the docker image:

docker pull axarev/parsr

If you also wish to install the GUI for sending documents and visualising results:

docker pull axarev/parsr-ui-localhost

Note: Parsr can also be installed bare-metal (not via Docker containers), the procedure for which is documented in the installation guide.

Usage

-- The advanced usage guide is available here --

To run the API, issue:

docker run -p 3001:3001 axarev/parsr

which will launch it on http://localhost:3001.
Consult the documentation on the usage of the API.

  1. To access the python client to Parsr API, issue:

    pip install parsr-client
    

    To sample the Jupyter Notebook, using the python client, head over to the jupyter demo.

  1. To use the GUI tool (the API needs to already be running), issue:
    docker run -t -p 8080:80 axarev/parsr-ui-localhost:latest
    
    Then, access it through http://localhost:8080.

Refer to the Configuration documentation to interpret the configurable options in the GUI viewer.

The API based usage and the command line usage are documented in the advanced usage guide.

Documentation

All documentation files can be found here.

Contribute

Please refer to the contribution guidelines.

Third Party Licenses

Third Party Libraries licenses for its dependencies:

  1. QPDF: Apache http://qpdf.sourceforge.net
  2. ImageMagick: Apache 2.0 https://imagemagick.org/script/license.php
  3. Pdfminer.six: MIT https://github.com/pdfminer/pdfminer.six/blob/master/LICENSE
  4. PDF.js: Apache 2.0 https://github.com/mozilla/pdf.js
  5. Tesseract: Apache 2.0 https://github.com/tesseract-ocr/tesseract
  6. Camelot: MIT https://github.com/camelot-dev/camelot
  7. MuPDF (Optional dependency): AGPL https://mupdf.com/license.html
  8. Pandoc (Optional dependency): GPL https://github.com/jgm/pandoc

License

Copyright 2020 AXA Group Operations S.A.
Licensed under the Apache 2.0 license (see the LICENSE file).

data
document
extraction
hacktoberfest
images
nlp
ocr
parsr
pdf
python
typescript

axa-group/Parsr

Transforms PDF, Documents and Images into Enriched Structured Data

JavaScript

6,169

1,872 commits

updated Mar 20, 2026

See the code

README


Turn your documents into data!

Français | Portuguese | Spanish | 中文

[!WARNING] This project is no longer maintained. Security patches are not being applied. Consider using an alternative such as LiteParse if you need a local, Apache 2.0–licensed parsing solution.

  • Parsr, is a minimal-footprint document (image, pdf, docx, eml) cleaning, parsing and extraction toolchain which generates readily available, organized and usable data in JSON, Markdown (MD), CSV/Pandas DF or TXT formats.

  • It provides analysts, data scientists and developers with clean structured and label-enriched information set for ready-to-use applications ranging from data entry and document analysts automation, archival, and many others.

  • Currently, Parsr can perform: document cleaning, hierarchy regeneration (words, lines, paragraphs), detection of headings, tables, lists, table of contents, page numbers, headers/footers, links, and others. Check out all the features.

Table of Contents

Getting Started

Installation

-- The advanced installation guide is available here --

The quickest way to install and run the Parsr API is through the docker image:

docker pull axarev/parsr

If you also wish to install the GUI for sending documents and visualising results:

docker pull axarev/parsr-ui-localhost

Note: Parsr can also be installed bare-metal (not via Docker containers), the procedure for which is documented in the installation guide.

Usage

-- The advanced usage guide is available here --

To run the API, issue:

docker run -p 3001:3001 axarev/parsr

which will launch it on http://localhost:3001.
Consult the documentation on the usage of the API.

  1. To access the python client to Parsr API, issue:

    pip install parsr-client
    

    To sample the Jupyter Notebook, using the python client, head over to the jupyter demo.

  1. To use the GUI tool (the API needs to already be running), issue:
    docker run -t -p 8080:80 axarev/parsr-ui-localhost:latest
    
    Then, access it through http://localhost:8080.

Refer to the Configuration documentation to interpret the configurable options in the GUI viewer.

The API based usage and the command line usage are documented in the advanced usage guide.

Documentation

All documentation files can be found here.

Contribute

Please refer to the contribution guidelines.

Third Party Licenses

Third Party Libraries licenses for its dependencies:

  1. QPDF: Apache http://qpdf.sourceforge.net
  2. ImageMagick: Apache 2.0 https://imagemagick.org/script/license.php
  3. Pdfminer.six: MIT https://github.com/pdfminer/pdfminer.six/blob/master/LICENSE
  4. PDF.js: Apache 2.0 https://github.com/mozilla/pdf.js
  5. Tesseract: Apache 2.0 https://github.com/tesseract-ocr/tesseract
  6. Camelot: MIT https://github.com/camelot-dev/camelot
  7. MuPDF (Optional dependency): AGPL https://mupdf.com/license.html
  8. Pandoc (Optional dependency): GPL https://github.com/jgm/pandoc

License

Copyright 2020 AXA Group Operations S.A.
Licensed under the Apache 2.0 license (see the LICENSE file).

data
document
extraction
hacktoberfest
images
nlp
ocr
parsr
pdf
python
typescript

Project health

Overall

4.3/10

Maintained

0/10

Code review

6/10
OpenSSF Scorecard

Significant stargazers

(top 24 of 36)

Albert Villanova del Moral

644 followers · starred Jan 2022

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1,795 followers · starred Feb 2020

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1,833 followers · starred Sep 2022

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325 followers · starred Feb 2026