Compound Figure Separator

Acknowledgement

This repository was forked from https://github.com/apple2373/figure-separator. If you find this tool useful, please cite the original paper:

@inproceedings{tsutsui2017data,
  title={A data driven approach for compound figure separation using convolutional neural networks},
  author={Tsutsui, Satoshi and Crandall, David J},
  booktitle={2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
  volume={1},
  pages={533--540},
  year={2017},
  organization={IEEE}
}

Getting started

Install requirements

$ pip install -r requirements.txt

Download the pre-trained model

The model can be found at

Separate compound figures

$ python split_figure.py --split --output ./results imgs/*

See the results directory. Output json is something like:

[
 {
 "x": (x coordinate of left top point of the sub-figure),
 "y": (y coordinate of left top point of the sub-figure),
 "w": (width of the sub-figure),
 "h": (height of the sub-figure),
 "conf": (confidence value of the extaction),
 } ,....
]

Here is other options:

$ python split_figure.py --help                                       
Usage:
    main.py [options] --output=<dir> <inputs>...

Options:
    --thresh <float>    sub-figure detection threshold. [default: 0.5]
    --model <file>      model pb file. [default: ./data/figure-separation-model-submitted-544.pb]
    --split             split the image.
    --overwrite         Overwrite.

Use the codes inside the project

from figure_separator import FigureSeparator
fig_separator=FigureSeparator(MODEL)
sub_figures=fig_separator.extract(IMAGE)
print(sub_figures)