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committedApr 2, 2022
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‎README.md

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<p align="center">
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<img src="https://github.com/Layout-Parser/layout-parser/raw/master/.github/layout-parser.png" alt="Layout Parser Logo" width="35%">
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<img src=".github/layout-parser.png" alt="Layout Parser Logo" width="35%">
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<h3 align="center">
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A unified toolkit for Deep Learning Based Document Image Analysis
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</h3>
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</p>
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<p align=center>
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<a href="https://pypi.org/project/layoutparser/"><img src="https://img.shields.io/pypi/v/layoutparser?color=%23099cec&label=PyPI%20package&logo=pypi&logoColor=white" title="The current version of Layout Parser"></a>
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<a href="https://github.com/Layout-Parser/layout-parser/blob/master/LICENSE"><img src="https://img.shields.io/pypi/l/layoutparser" title="Layout Parser uses Apache 2 License"></a>
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<a href="https://github.com/Layout-Parser/layout-parser/blob/main/LICENSE"><img src="https://img.shields.io/pypi/l/layoutparser" title="Layout Parser uses Apache 2 License"></a>
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<img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/layoutparser">
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</p>
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We provide a series of examples for to help you start using the layout parser library:
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1. [Table OCR and Results Parsing](https://github.com/Layout-Parser/layout-parser/blob/master/examples/OCR%20Tables%20and%20Parse%20the%20Output.ipynb): `layoutparser` can be used for conveniently OCR documents and convert the output in to structured data.
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1. [Table OCR and Results Parsing](https://github.com/Layout-Parser/layout-parser/blob/main/examples/OCR%20Tables%20and%20Parse%20the%20Output.ipynb): `layoutparser` can be used for conveniently OCR documents and convert the output in to structured data.
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2. [Deep Layout Parsing Example](https://github.com/Layout-Parser/layout-parser/blob/master/examples/Deep%20Layout%20Parsing.ipynb): With the help of Deep Learning, `layoutparser` supports the analysis very complex documents and processing of the hierarchical structure in the layouts.
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2. [Deep Layout Parsing Example](https://github.com/Layout-Parser/layout-parser/blob/main/examples/Deep%20Layout%20Parsing.ipynb): With the help of Deep Learning, `layoutparser` supports the analysis very complex documents and processing of the hierarchical structure in the layouts.
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## Contributing
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‎docs/example/load_coco/index.rst

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We could also check how the trained layout model performs on the input
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image. Following this
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`instruction <https://github.com/Layout-Parser/layout-parser/blob/master/examples/Deep%20Layout%20Parsing.ipynb>`__,
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`instruction <https://github.com/Layout-Parser/layout-parser/blob/main/examples/Deep%20Layout%20Parsing.ipynb>`__,
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we could conveniently load a layout prediction model and run predictions
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on the existing image.
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‎examples/Load and visualize layout annotations in the COCO format.ipynb

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