layoutparser 0.2.0

Layout Parser is a deep learning assisted tool for Document Image Layout Analysis.

A unified toolkit for Deep Learning Based Document Image Analysis

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Installation

You can find detailed installation instructions in installation.md. But generally, it's just pip install some libraries:

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 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: layoutparser can be used for conveniently OCR documents and convert the output in to structured data.

2. Deep Layout Parsing Example: 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

The images shown in the figure above are: a screenshot of this paper, an image from the PRIMA Layout Analysis Dataset, a screenshot of the WSJ website, and an image from the 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, or load the model that you trained on your own. And use the following code to predict the layout as well as visualize it:

>>> 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 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 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}
}