Metadata-Version: 2.1
Name: layoutparser
Version: 0.2.0
Summary: Layout Parser is a deep learning assisted tool for Document Image Layout Analysis.
Home-page: https://github.com/Layout-Parser/layout-parser
Author: Zejiang Shen Ruochen Zhang
License: Apache-2.0
Description: <p align="center">
          <img src="https://github.com/Layout-Parser/layout-parser/raw/master/.github/layout-parser.png" alt="Layout Parser Logo" width="35%">
          <p align="center">
          A unified toolkit for Deep Learning Based Document Image Analysis
          </p>
        </p>
        
        <p align=center>
        <a href="https://arxiv.org/abs/2103.15348"><img src="https://img.shields.io/badge/arXiv-2103.15348-b31b1b.svg" title="Layout Parser Paper"></a>
        <a href="https://layout-parser.github.io"><img src="https://img.shields.io/badge/website-layout--parser.github.io-informational.svg" title="Layout Parser Paper"></a>
        <a href="https://layout-parser.readthedocs.io/en/latest/"><img src="https://img.shields.io/badge/doc-layout--parser.readthedocs.io-light.svg" title="Layout Parser Documentation"></a>
        </p>
        
        <p align=center>
        <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>
        <a href="https://pypi.org/project/layoutparser/"><img src="https://img.shields.io/pypi/pyversions/layoutparser?color=%23099cec&" alt="Python 3.6 3.7 3.8" title="Layout Parser supports Python 3.6 and above"></a>
        <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/layoutparser">
        <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>
        </p>
        
        ---
        
        ## Installation 
        
        You can find detailed installation instructions in [installation.md](installation.md). But generally, it's just `pip install` 
        some libraries: 
        
        ```bash
        pip install -U layoutparser
        
        # Install Detectron2 for using DL Layout Detection Model
        # Please make sure the PyTorch version is compatible with
        # the installed Detectron2 version. 
        pip install 'git+https://github.com/facebookresearch/detectron2.git@v0.4#egg=detectron2' 
        
        # Install the ocr components when necessary 
        pip install layoutparser[ocr]      
        ```
        
        **For Windows Users:** Please read [installation.md](installation.md) for details about installing Detectron2.
        
        ## Quick Start
        
        We provide a series of examples for to help you start using the layout parser library: 
        
        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. 
        
        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. 
        
        
        ## DL Assisted Layout Prediction Example 
        
        ![Example Usage](.github/example.png)
        
        *The images shown in the figure above are: a screenshot of [this paper](https://arxiv.org/abs/2004.08686), an image from the [PRIMA Layout Analysis Dataset](https://www.primaresearch.org/dataset/), a screenshot of the [WSJ website](http://wsj.com), and an image from the [HJDataset](https://dell-research-harvard.github.io/HJDataset/).*
        
        With only 4 lines of code in `layoutparse`, you can unlock the information from complex documents that existing tools could not provide. You can either choose a deep learning model from the [ModelZoo](https://github.com/Layout-Parser/layout-parser/blob/master/docs/notes/modelzoo.md), or load the model that you trained on your own. And use the following code to predict the layout as well as visualize it: 
        
        ```python
        >>> import layoutparser as lp
        >>> model = lp.Detectron2LayoutModel('lp://PrimaLayout/mask_rcnn_R_50_FPN_3x/config')
        >>> layout = model.detect(image) # You need to load the image somewhere else, e.g., image = cv2.imread(...)
        >>> lp.draw_box(image, layout,) # With extra configurations
        ```
        
        ## Contributing
        
        We encourage you to contribute to Ruby on Rails! Please check out the [Contributing guidelines](.github/CONTRIBUTING.md) for guidelines about how to proceed. Join us!
        
        ## Citing `layoutparser`
        
        If you find `layoutparser` helpful to your work, please consider citing our tool and [paper](https://arxiv.org/pdf/2103.15348.pdf) using the following BibTeX entry.
        
        ```
        @article{shen2021layoutparser,
          title={LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis},
          author={Shen, Zejiang and Zhang, Ruochen and Dell, Melissa and Lee, Benjamin Charles Germain and Carlson, Jacob and Li, Weining},
          journal={arXiv preprint arXiv:2103.15348},
          year={2021}
        }
        ```
Keywords: layout analysis,deep learning
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: ocr
