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Layout Parser is deep learning based tool for document image layout analysis tasks.

Installation

Use pip or conda to install the library:

pip install layoutparser

# Install Detectron2 for using DL Layout Detection Model
pip install 'git+https://github.com/facebookresearch/detectron2.git@v0.1.3#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.

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

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://HJDataset/faster_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, box_width=3)