Metadata-Version: 2.1
Name: layoutparser
Version: 0.1.3
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="left">
          <img src="https://github.com/Layout-Parser/layout-parser/raw/master/.github/layout-parser.png" alt="Layout Parser Logo" width="35%">
        </p>
        
        <p align="center">
        
        [![Docs](https://readthedocs.org/projects/layout-parser/badge/)](https://layout-parser.readthedocs.io/en/latest/)
        [![PyPI](https://img.shields.io/pypi/v/layoutparser?color=%23099cec&label=PyPI%20package&logo=pypi&logoColor=white)](https://pypi.org/project/layoutparser/)
        [![PyVersion](https://img.shields.io/pypi/pyversions/layoutparser?color=%23099cec&
        )](https://pypi.org/project/layoutparser/)
        [![License](https://img.shields.io/pypi/l/layoutparser)](https://github.com/Layout-Parser/layout-parser/blob/master/LICENSE)
        
        </p>
        
        ---
        
        Layout Parser is deep learning based tool for document image layout analysis tasks. 
        
        ## Installation 
        
        Use pip or conda to install the library:
        ```bash
        pip install 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#egg=detectron2' 
        
        # Install the ocr components when necessary 
        pip install layoutparser[ocr]      
        ```
        This by default will install the CPU version of the Detectron2, and it should be able to run on most of the computers. But if you have a GPU, you can consider the GPU version of the Detectron2, referring to the [official instructions](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md).
        
        ## 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
        ```
        
        ## Citing `layoutparser`
        
        If you find `layoutparser` helpful to your work, please consider citing our tool using the following BibTeX entry.
        
        ```
        @misc{shen2020layoutparser,
          author = {Zejiang Shen and Ruochen Zhang and Melissa Dell},
          title = {LayoutParser},
          howpublished = {\url{https://github.com/Layout-Parser/layout-parser}},
          year = {2020}
        }
        ```
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: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: ocr
