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Code for: S.R. Qasim, H. Mahmood, and F. Shafait, Rethinking Table Recognition using Graph Neural Networks (2019)
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# TIES-2.0
TIES was my undergraduate thesis, Table Information Extraction System. I picked the name from there and made it 2.0
from there.
This is a repository containing source code for the arxiv paper 1905.13391 ([link](https://arxiv.org/pdf/1905.13391.pdf)). This paper has been accepted into
ICDAR 2019. To cite the paper, use:
```
@article{rethinkingGraphs,
author = {Qasim, Shah Rukh and Mahmood, Hassan and Shafait, Faisal},
title = {Rethinking Table Recognition using Graph Neural Networks},
journal = {Accepted into ICDAR 2019},
volume = {abs/1905.13391},
year = {2019},
url = {https://arxiv.org/abs/1905.13391},
archivePrefix = {arXiv},
eprint = {1905.13391},
}
```
## Note to the visitors
We are still working to improve a few technical details for your convenience. We'll remove this note once we are done.
Expect them to be done by June 15, 2019. We are also working to improve dataset format for easier understanding.
## Dataset
Partial dataset which was used for test can be found [here](https://drive.google.com/drive/folders/18QyBB1pavj_xCsTyCR6XC_AA525nZaVZ?usp=sharing
). We are uploading rest of the dataset. The current format of the dataset is `tfrecords`.
In the meantime, if you want to generate the dataset, head on to the following repository:
[github.com/hassan-mahmood/Structural_Analysis](https://github.com/hassan-mahmood/Structural_Analysis)
## Development note
The project is divided into language parts, `python` and `cpp`, for python and C++ respectively. There is nothing in the
`cpp` folder as of now.
The `python` dir is supposed to be the path where a script is to be run, or alternatively, it could be added to the
`$PYTHONPATH` environmental variable. It would contain further directories:
1. `bin` contain the scripts which are to be run from the terminal. Within bin, there would be multiple folders,
short for different classes of executable programs.
1. `iterate` for running training or inference.
2. `analyse` for analysing inference output.
3. `checks` this was for testing various files while development. You can safely ignore it.
2. `iterators` provides functionality to iterate through the datasets while you are training or testing.
3. `layers` contains basic layers for graph networks
3. `models` contains the main model and network segments. Most of the functionality can be found in `basic_model.py`.
Start to trace from there.
4. `ops` contains basic modified operations. These contains the advanced graph operations code.
5. `readers` is for readers, entities responsible for reading the data from `tfrecords`. Their format can be changed in
this file.
6. `libs` contains all other helper and library functions.
Within the context of this repository, `iterate` refers to any of train, test or anything which is done iteratively. You
can say anything that is done iteratively mostly on the GPU. So if there is an `iterator` somewhere, it probably refers
to an entity which handles training, testing etc.
## Preparation
1. Prepare the dataset. For this, you are required to divide the dataset into three different sections, test, train and validation.
Test set will be used to run the analysis after training is done. Backpropagation will be run on the train set. Validation set is
used to produce plots for tensorboard to monitor performance of the network.
2. The dataset files have to be in `tfrecords` format. Make a new file called `train_files.txt`. It should contain full paths of all the training tfrecords files. For example:
```
/home/shahrukhqasim/dataset/train_1.tfrecord
/home/shahrukhqasim/dataset/train_2.tfrecord
/home/shahrukhqasim/dataset/train_3.tfrecord
```
3. Similarly, prepare `validation_files.txt`, `test_files.txt`. The contents of these three files should not be overlapping.
4. Make a config file according to the format given in `configs/config.ini.example`. This file determines all the settings, dataset locations andExcerpt of 6,733 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c2f3d93f711f61c3, llm:Repository description and README state: 'Rethinking Table Recognition using Graph Neural Networks', topics include 'table-reco', 'computer', 'icdar', 'machine-learning'; code implements ICDAR 2019 paper for table information extraction.
matched fp:c2f3d93f711f61c3, llm:Repository description and README state: 'Rethinking Table Recognition using Graph Neural Networks', topics include 'table-reco', 'computer', 'icdar', 'machine-learning'; code implements ICDAR 2019 paper for table information extraction.
matched fp:c2f3d93f711f61c3, llm:Repository description and README state: 'Rethinking Table Recognition using Graph Neural Networks', topics include 'table-reco', 'computer', 'icdar', 'machine-learning'; code implements ICDAR 2019 paper for table information extraction.