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https://github.com/doc-analysis/TableBank

TableBank: A Benchmark Dataset for Table Detection and Recognition
https://github.com/doc-analysis/TableBank

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TableBank: A Benchmark Dataset for Table Detection and Recognition

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# TableBank



TableBank is a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet, contains 417K high-quality labeled tables.



## News

- **We update the license to Apache-2.0.**

- **We release an official split for the train/val/test datasets and re-train both of the Table Detection and Table Structure Recognition models using Detectron2 and OpenNMT tools. The benchmark results, the MODEL ZOO, and the download link of TableBank have been updated.**

- **A new benchmark dataset DocBank ([Paper](https://arxiv.org/abs/2006.01038), [Repo](https://github.com/doc-analysis/DocBank)) is now available for document layout analysis**

- **Our data can only be used for research purpose**

- **Our paper has been accepted in [LREC 2020](https://lrec2020.lrec-conf.org/en/conference-programme/accepted-papers/)**



## Introduction

To address the need for a standard open domain table

benchmark dataset, we propose a novel weak supervision approach

to automatically create the TableBank, which is orders

of magnitude larger than existing human labeled datasets for

table analysis. Distinct from traditional weakly supervised

training set, our approach can obtain not only large scale but

also high quality training data.



Nowadays, there are a great

number of electronic documents on the web such as Microsoft

Word (.docx) and Latex (.tex) files. These online documents

contain mark-up tags for tables in their source code by nature.

Intuitively, we can manipulate these source code by adding

bounding box using the mark-up language within each document.

For Word documents, the internal Office XML code

can be modified where the borderline of each table is identified.

For Latex documents, the tex code can be also modified

where bounding boxes of tables are recognized. In this

way, high-quality labeled data is created for a variety of domains

such as business documents, official fillings, research

papers etc, which is tremendously beneficial for large-scale

table analysis tasks.



The TableBank dataset totally consists of 417,234 high

quality labeled tables as well as their original documents in

a variety of domains.



### Statistics of TableBank

#### Based on the number of tables

| Task                        | Word    | Latex   | Word+Latex |

|-----------------------------|---------|---------|------------|

| Table detection             | 163,417 | 253,817 | 417,234    |

| Table structure recognition | 56,866  | 88,597  | 145,463    | 



#### Based on the number of images

| Task                        | Word    | Latex   | Word+Latex |

|-----------------------------|---------|---------|------------|

| Table detection             | 78,399  | 200,183 | 278,582    |

| Table structure recognition | 56,866  | 88,597  | 145,463    |



#### Statistics on Train/Val/Test sets of Table Detection

| Source | Train  | Val   | Test |

|--------|--------|-------|------|

| Latex  | 187199 |  7265 | 5719 |

| Word   |  73383 |  2735 | 2281 |

| Total  | 260582 | 10000 | 8000 |



#### Statistics on Train/Val/Test sets of Table Structure Recognition

| Source | Train  | Val   | Test |

|--------|--------|-------|------|

| Latex  |  79486 |  6075 | 3036 |

| Word   |  50977 |  3925 | 1964 |

| Total  | 130463 | 10000 | 5000 |



## Task Definition



### Table Detection

Table detection aims to locate tables using bounding boxes

in a document. 

Given a document page in the image format,

generating several bounding box that represents the

location of tables in this page.



### Table Structure Recognition

Table structure recognition aims to identify the row and column

layout structure for the tables especially in non-digital

document formats such as scanned images.

Given a table in the image format,

generating an HTML tag sequence that represents the

arrangement of rows and columns as well as the type of table

cells.



## Baselines

To verify the effectiveness of Table-Bank,

we build several strong baselines using the state-of-the-art

models with end-to-end deep neural networks. The

table detection model is based on the Faster R-CNN [Ren

et al., 2015] architecture with different settings. The table

structure recognition model is based on the encoder-decoder

framework for image-to-text.

### Data and Metrics

To evaluate

table detection, we sample 18,000 document images from

Word and Latex documents, where 10,000 images

for validation and 8,000 images for testing. Each sampled image

contains at least one table. Meanwhile, we also evaluate

our model on the ICDAR 2013 dataset to verify the effectiveness

of TableBank. To evaluate table structure recognition,

we sample 15,000 table images from

Word and Latex documents, where 10,000 images

for validation and 5,000 images for testing. 

For table detection, we calculate the precision, recall

and F1 in the way described in our paper, where

the metrics for all documents are computed by summing up

the area of overlap, prediction and ground truth. For table

structure recognition, we use the 4-gram BLEU score as the

evaluation metric with a single reference.



### Table Detection

We use the open-source framework Detectron2

[Wu et al., 2019] to train models on the TableBank.

Detectron2 is a high-quality and high-performance codebase

for object detection research, which supports many state-of-the-art

algorithms. In this task, we use the Faster R-CNN algorithm

with the ResNeXt [Xie et al., 2016] as the backbone

network architecture, where the parameters are pre-trained on

the ImageNet dataset. All baselines are trained using 4 V100

NVIDIA GPUs using data-parallel sync SGD with a minibatch

size of 20 images. For other parameters, we use the

default values in Detectron2. During testing, the confidence

threshold of generating bounding boxes is set to 90%.



|      Models      |    Word   |        |        |   Latex   |        |        | Word+Latex |        |        |

|:----------------:|:---------:|:------:|:------:|:---------:|:------:|:------:|:----------:|:------:|:------:|

|                  | Precision | Recall |   F1   | Precision | Recall |   F1   |  Precision | Recall |   F1   |

|    X101(Word)    |   0.9352  | 0.9398 | 0.9375 |   0.9905  | 0.5851 | 0.7356 |   0.9579   | 0.7474 | 0.8397 |

|    X152(Word)    |   0.9418  | 0.9415 | **0.9416** |   0.9912  | 0.6882 | 0.8124 |   0.9641   | 0.8041 | 0.8769 |

|    X101(Latex)   |   0.8453  | 0.9335 | 0.8872 |   0.9819  | 0.9799 | 0.9809 |   0.9159   | 0.9587 | 0.9368 |

|    X152(Latex)   |   0.8476  | 0.9264 | 0.8853 |   0.9816  | 0.9814 | **0.9815** |   0.9173   | 0.9562 | 0.9364 |

| X101(Word+Latex) |   0.9178  | 0.9363 | 0.9270 |   0.9827  | 0.9784 | 0.9806 |   0.9526   | 0.9592 | **0.9559** |

| X152(Word+Latex) |   0.9229  | 0.9266 | 0.9247 |   0.9837  | 0.9752 | 0.9795 |   0.9557   | 0.9530 | 0.9543 |



### Table Structure Recognition

For table structure recognition, we use the open-source

framework OpenNMT [Klein et al., 2017] to train the image-to-text model.

OpenNMT is mainly designed for neural

machine translation, which supports many encoder-decoder

frameworks. In this task, we train our model using the image-to-text

method in OpenNMT. The model is also trained using

4 V100 NVIDIA GPUs with the learning rate of 1

and batch size of 24. For other parameters,

we use the default values in OpenNMT.



|           Models           |  Word | Latex | Word+Latex |

|:--------------------------:|:-----:|:-----:|:----------:|

|    Image-to-Text (Word)    | 59.18 | 69.76 |    65.75   |

|    Image-to-Text (Latex)   | 51.45 | 71.63 |    63.08   |

| Image-to-Text (Word+Latex) | **69.93** | **77.94** |    **74.54**   |



## Model Zoo



The trained models are available for download in the [TableBank Model Zoo](MODEL_ZOO.md).



## Get Data and Leaderboard



**\*\*Please DO NOT re-distribute our data.\*\***



If you use the corpus in published work, please cite it referring to the "Paper and Citation" Section.



The annotations and original document pictures of the TableBank dataset **can be download from the [TableBank dataset homepage](https://doc-analysis.github.io/tablebank-page/index.html)**.



## Paper and Citation

https://arxiv.org/abs/1903.01949

```

@misc{li2019tablebank,

    title={TableBank: A Benchmark Dataset for Table Detection and Recognition},

    author={Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou and Zhoujun Li},

    year={2019},

    eprint={1903.01949},

    archivePrefix={arXiv},

    primaryClass={cs.CV}

}

```



## References



- [Ren et al., 2015] Shaoqing Ren, Kaiming He, Ross B. Girshick,

    and Jian Sun. Faster R-CNN: towards real-time

    object detection with region proposal networks. CoRR,

    abs/1506.01497, 2015.

- [Gilani et al., 2017] A. Gilani, S. R. Qasim, I. Malik, and

    F. Shafait. Table detection using deep learning. In Proc. of

    ICDAR 2017, volume 01, pages 771–776, Nov 2017.

- [Wu et al., 2019] Y Wu, A Kirillov, F Massa, WY Lo, R Girshick. [Detectron2](https://github.com/facebookresearch/detectron2)[J]. 2019.

- [Xie et al., 2016] Saining Xie, Ross B. Girshick, Piotr

    Doll´ar, Zhuowen Tu, and Kaiming He. Aggregated residual

    transformations for deep neural networks. CoRR,

    abs/1611.05431, 2016.

- [Klein et al., 2017] Guillaume Klein, Yoon Kim, Yuntian

    Deng, Jean Senellart, and Alexander M. Rush. Open-NMT:

    Open-source toolkit for neural machine translation.

    In Proc. of ACL, 2017.]