A table reconstruction package
Project description
Table Reconstruction
table-reconstruction
is a tool used to detect table spaces and reconstruct the information in them using DL models.
To provide the above feature, Table reconstruction works based on several components as follows:
- A table detection model is developed based on Yolov5
- A line segmentation model is built based on Unet
- Additional modules are used in the information extraction process, especially a directed graph is used to extract information related to the merged cells.
Installation
Table Reconstruction is published on PyPI and can be installed from there:
pip install table-reconstruction
You can also install this package manually with the following command:
python setup.py install
Basic usage
you can easily use this library by using the following statements
import torch
from table_reconstruction import TableExtraction
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
extraction = TableExtraction(device=device)
image = ... # Accept Numpy ndarray and PIL image
tables = extraction.extract(image)
We also provide a simple Jupyter notebook which can be used to illustrate the results obtained after processing, please check it out here
Documentation
Documentation will be available soon.
Get in touch
- Report bugs, suggest features or view the source code on GitHub.
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