| Name | doctr-labeler JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | None |
| Summary | A Python package for labeling and annotating documents |
| upload_time | 2025-01-24 13:25:08 |
| maintainer | Felix Dittrich |
| docs_url | None |
| author | None |
| requires_python | <4,>=3.10.0 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
ocr
document processing
labeling
annotation
doctr
onnxtr
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<img src="https://github.com/text2knowledge/docTR-Labeler/raw/main/docs/images/logo.png" width="40%">
</p>
[](LICENSE)
[](https://codecov.io/gh/text2knowledge/docTR-Labeler)
[](https://www.codefactor.io/repository/github/text2knowledge/doctr-labeler)
[](https://pypi.org/project/docTR-Labeler/)
docTR Labeler is a tool to label OCR data for the [docTR](https://github.com/mindee/doctr) and [OnnxTR](https://github.com/felixdittrich92/OnnxTR) projects.
**Attention**: This project is still in development - and currently a pre-release version - please report any issues you encounter.
What you can expect from this repository:
- Efficient way to label OCR data
- Features like auto-annotation using [OnnxTR](https://github.com/felixdittrich92/OnnxTR) and auto polygon adjustment
- Easy to use frontend with keybindings
- CLI and programmatic usage
- No Login required
<p align="center">
<img src="https://github.com/text2knowledge/docTR-Labeler/raw/main/docs/images/ui_screen.png" width="90%">
</p>
## Installation
### Prerequisites
Python 3.10 (or higher) and [pip](https://pip.pypa.io/en/stable/) are required to install docTR-Labeler.
### Latest release
You can then install the latest release of the package using [pypi](https://pypi.org/project/OnnxTR/) as follows:
```bash
pip3 install doctr-labeler
```
## Keybindings
- `Ctrl + a` : Select all polygons
- `Esc` : Deselect all selected polygons
- `Ctrl + t` : Auto adjust the selected polygons
- `Ctrl + r` : Reset last auto adjustment
- `Ctrl + s` : Save the current progress / image annotation
- `Ctrl + d` : Delete the selected polygon
- `Ctrl + f` : Draw a new polygon
- `Ctrl + c` : Undo while drawing a polygon
- `Ctrl + +` : Zoom in (up to 150% by default) - Can be changed by setting a environment variable `DOCTR_LABELER_MAX_ZOOM` to a value between 1.1 and 2.0
- `Ctrl + -` : Zoom out (down to 50% by default) - Can be changed by setting a environment variable `DOCTR_LABELER_MIN_ZOOM` to a value between 0.1 and 0.9
## Usage CLI
After installation you can use the CLI to start the tool:
For this open a terminal and run:
```bash
doctr-labeler
```
## Usage Programmatic
You can also use the tool programmatic:
```python
from labeler.views import GUI
from labeler.utils import prepare_data_folder, hf_upload_dataset
# (Optional)
# Prepare the data folder you can pass a path to a folder containing images and PDFs
# The function will create a new folder 'images' with the prepared data
prepared_data_path = prepare_data_folder("path/to/folder")
# Start the GUI
gui = GUI(image_folder=prepared_data_path)
gui.start_gui()
# or if you want to annotate also for KIE
types = ["Total", "Date", "Invoice Number", "VAT Number", "Address", "Company Name"]
gui = GUI(image_folder=prepared_data_path, text_types=types)
gui.start_gui()
# (Optional) Upload the prepared data to the Hugging Face dataset hub
# The path to the folder should contain an 'images' folder and it's corresponding 'labels.json' file or the 'tmp
hf_upload_dataset(prepared_data_path)
```
## Credits
- This project is based on the [Form-Labeller](https://github.com/devarshi16/Form-Labeller) project by Devarshi Aggarwal.
## Citation
If you wish to cite please refer to the base project citation, feel free to use this [BibTeX](http://www.bibtex.org/) references:
```bibtex
@misc{docTR-Labeler,
title={docTR Labeler: docTR OCR Annotation Tool},
author={{Dittrich, Felix}, {List, Ian}},
year={2024},
publisher = {GitHub},
howpublished = {\url{https://github.com/text2knowledge/docTR-Labeler}}
}
```
```bibtex
@misc{Form-Labeller,
author = {Aggarwal, Devarshi},
title = {{Form Labeller}},
howpublished = {\url{https://github.com/devarshi16/Form-Labeller}},
year = {2020},
note = {Online; accessed 01-March-2020}
}
```
## Contributing
Contributions are what make the open-source community such an amazing place to learn, inspire, and create.
Any contributions you make are **greatly appreciated**.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Add your Changes
4. Run the tests and quality checks (`make test` and `make style` and `make quality`)
5. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
6. Push to the Branch (`git push origin feature/AmazingFeature`)
## License
Distributed under the Apache 2.0 License. See [`LICENSE`](https://github.com/felixdittrich92/OnnxTR?tab=Apache-2.0-1-ov-file#readme) for more information.
Raw data
{
"_id": null,
"home_page": null,
"name": "doctr-labeler",
"maintainer": "Felix Dittrich",
"docs_url": null,
"requires_python": "<4,>=3.10.0",
"maintainer_email": null,
"keywords": "OCR, document processing, labeling, annotation, docTR, OnnxTR",
"author": null,
"author_email": "Felix Dittrich <felixdittrich92@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/93/d5/5931aefbf19a7ee0896a6777e2bc87b57f8fde2cdf3a39faee132bc372f5/doctr_labeler-0.1.3.tar.gz",
"platform": null,
"description": "<p align=\"center\">\n <img src=\"https://github.com/text2knowledge/docTR-Labeler/raw/main/docs/images/logo.png\" width=\"40%\">\n</p>\n\n[](LICENSE)\n\n[](https://codecov.io/gh/text2knowledge/docTR-Labeler)\n[](https://www.codefactor.io/repository/github/text2knowledge/doctr-labeler)\n[](https://pypi.org/project/docTR-Labeler/)\n\ndocTR Labeler is a tool to label OCR data for the [docTR](https://github.com/mindee/doctr) and [OnnxTR](https://github.com/felixdittrich92/OnnxTR) projects.\n\n**Attention**: This project is still in development - and currently a pre-release version - please report any issues you encounter.\n\nWhat you can expect from this repository:\n\n- Efficient way to label OCR data\n- Features like auto-annotation using [OnnxTR](https://github.com/felixdittrich92/OnnxTR) and auto polygon adjustment\n- Easy to use frontend with keybindings\n- CLI and programmatic usage\n- No Login required\n\n<p align=\"center\">\n <img src=\"https://github.com/text2knowledge/docTR-Labeler/raw/main/docs/images/ui_screen.png\" width=\"90%\">\n</p>\n\n## Installation\n\n### Prerequisites\n\nPython 3.10 (or higher) and [pip](https://pip.pypa.io/en/stable/) are required to install docTR-Labeler.\n\n### Latest release\n\nYou can then install the latest release of the package using [pypi](https://pypi.org/project/OnnxTR/) as follows:\n\n```bash\npip3 install doctr-labeler\n```\n\n## Keybindings\n\n- `Ctrl + a` : Select all polygons\n- `Esc` : Deselect all selected polygons\n- `Ctrl + t` : Auto adjust the selected polygons\n- `Ctrl + r` : Reset last auto adjustment\n- `Ctrl + s` : Save the current progress / image annotation\n- `Ctrl + d` : Delete the selected polygon\n- `Ctrl + f` : Draw a new polygon\n- `Ctrl + c` : Undo while drawing a polygon\n\n- `Ctrl + +` : Zoom in (up to 150% by default) - Can be changed by setting a environment variable `DOCTR_LABELER_MAX_ZOOM` to a value between 1.1 and 2.0\n- `Ctrl + -` : Zoom out (down to 50% by default) - Can be changed by setting a environment variable `DOCTR_LABELER_MIN_ZOOM` to a value between 0.1 and 0.9\n\n## Usage CLI\n\nAfter installation you can use the CLI to start the tool:\n\nFor this open a terminal and run:\n\n```bash\ndoctr-labeler\n```\n\n## Usage Programmatic\n\nYou can also use the tool programmatic:\n\n```python\nfrom labeler.views import GUI\nfrom labeler.utils import prepare_data_folder, hf_upload_dataset\n\n# (Optional)\n# Prepare the data folder you can pass a path to a folder containing images and PDFs\n# The function will create a new folder 'images' with the prepared data\nprepared_data_path = prepare_data_folder(\"path/to/folder\")\n\n# Start the GUI\ngui = GUI(image_folder=prepared_data_path)\ngui.start_gui()\n\n# or if you want to annotate also for KIE\ntypes = [\"Total\", \"Date\", \"Invoice Number\", \"VAT Number\", \"Address\", \"Company Name\"]\ngui = GUI(image_folder=prepared_data_path, text_types=types)\ngui.start_gui()\n\n# (Optional) Upload the prepared data to the Hugging Face dataset hub\n# The path to the folder should contain an 'images' folder and it's corresponding 'labels.json' file or the 'tmp\nhf_upload_dataset(prepared_data_path)\n```\n\n## Credits\n\n- This project is based on the [Form-Labeller](https://github.com/devarshi16/Form-Labeller) project by Devarshi Aggarwal.\n\n## Citation\n\nIf you wish to cite please refer to the base project citation, feel free to use this [BibTeX](http://www.bibtex.org/) references:\n\n```bibtex\n@misc{docTR-Labeler,\n title={docTR Labeler: docTR OCR Annotation Tool},\n author={{Dittrich, Felix}, {List, Ian}},\n year={2024},\n publisher = {GitHub},\n howpublished = {\\url{https://github.com/text2knowledge/docTR-Labeler}}\n}\n```\n\n```bibtex\n@misc{Form-Labeller,\n author = {Aggarwal, Devarshi},\n title = {{Form Labeller}},\n howpublished = {\\url{https://github.com/devarshi16/Form-Labeller}},\n year = {2020},\n note = {Online; accessed 01-March-2020}\n}\n```\n\n## Contributing\n\nContributions are what make the open-source community such an amazing place to learn, inspire, and create.\n\nAny contributions you make are **greatly appreciated**.\n\n1. Fork the Project\n2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)\n3. Add your Changes\n4. Run the tests and quality checks (`make test` and `make style` and `make quality`)\n5. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)\n6. Push to the Branch (`git push origin feature/AmazingFeature`)\n\n## License\n\nDistributed under the Apache 2.0 License. See [`LICENSE`](https://github.com/felixdittrich92/OnnxTR?tab=Apache-2.0-1-ov-file#readme) for more information.\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. 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If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n \n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n \n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n \n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n \n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n \n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. 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Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n \n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n \n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n \n 8. Limitation of Liability. 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