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Layout-Parser/layout-parser
A Unified Toolkit for Deep Learning Based Document Image Analysis
About Layout-Parser/layout-parser
Layout-Parser/layout-parser is an open-source project on GitHub, mainly written in Python. A Unified Toolkit for Deep Learning Based Document Image Analysis It currently holds 5,779 stars and 0 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).
Project Overview
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GitHub Repository Details
README
A unified toolkit for Deep Learning Based Document Image Analysis
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What is LayoutParser
LayoutParser aims to provide a wide range of tools that aims to streamline Document Image Analysis (DIA) tasks. Please check the LayoutParser demo video (1 min) or full talk (15 min) for details. And here are some key features:
- LayoutParser provides a rich repository of deep learning models for layout detection as well as a set of unified APIs for using them. For example,
Perform DL layout detection in 4 lines of code
import layoutparser as lp
model = lp.AutoLayoutModel('lp://EfficientDete/PubLayNet')
# image = Image.open("path/to/image")
layout = model.detect(image)
- LayoutParser comes with a set of layout data structures with carefully designed APIs that are optimized for document image analysis tasks. For example,
Selecting layout/textual elements in the left column of a page
image_width = image.size[0]
left_column = lp.Interval(0, image_width/2, axis='x')
layout.filter_by(left_column, center=True) # select objects in the left column
Performing OCR for each detected Layout Region
ocr_agent = lp.TesseractAgent()
for layout_region in layout:
image_segment = layout_region.crop(image)
text = ocr_agent.detect(image_segment)
Flexible APIs for visualizing the detected layouts
lp.draw_box(image, layout, box_width=1, show_element_id=True, box_alpha=0.25)
Loading layout data stored in json, csv, and even PDFs
```python layout = lp.load_json("path/to/json") layout = lp.load_csv("path/to/csv") pdf_layout = lp.load_pdf("path/to/pdf")
- LayoutParser is also a open platform that enables the sharing of layout detection models and DIA pipelines among the community.
Check the LayoutParser open platform
Submit your models/pipelines to LayoutParser
Installation
After several major updates, layoutparser provides various functionalities and deep learning models from different backends. But it still easy to install layoutparser, and we designed the installation method in a way such that you can choose to install only the needed dependencies for your project:
bash pip install layoutparser # Install the base layoutparser library with pip install "layoutparser[layoutmodels]" # Install DL layout model toolkit pip install "layoutparser[ocr]" # Install OCR toolkit
Extra steps are needed if you want to use Detectron2-based models. Please check installation.md for additional details on layoutparser installation.
Examples
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.
Contributing
We encourage you to contribute to Layout Parser! 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.
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