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https://github.com/abdoelsayed2016/TNCR_Dataset

Deep learning, Convolutional neural networks, Image processing, Document processing, Table detection, Page object detection, Table classification. https://www.sciencedirect.com/science/article/pii/S0925231221018142
https://github.com/abdoelsayed2016/TNCR_Dataset

classification cnn convolutional-neural-networks deep-learning image-classification mmdetection object-detection object-recognition pytorch table table-detection table-recognition

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Deep learning, Convolutional neural networks, Image processing, Document processing, Table detection, Page object detection, Table classification. https://www.sciencedirect.com/science/article/pii/S0925231221018142

Awesome Lists containing this project

README 

        
# TNCR: Table Net Detection and Classification Dataset 

[![PWC](https://img.shields.io/badge/PyTorch-v1.8-red)](https://pytorch.org/)

[![PWC](https://img.shields.io/badge/%20mmdetection%20-v2.10-red)](https://github.com/open-mmlab/mmdetection)



> **TNDR: Table Net Detection and Classification Dataset**


> [Abdelrahman Abdallah](https://github.com/abdoelsayed2016),

> [Alexander Berendeev](),

> [Islam Nuradin](),

> [Daniyar Nurseitov](),

> 



    



## Abstract 

We present TNCR, a new table dataset with varying image quality collected from open access websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. 



TNCR contains 9428 labeled tables with approximately 6621 images . In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Deformable DERT  with Resnet-50 Backbone Network achieves the highest performance compared to other methods with a precision of 86.7\%, recall of 89.6\%, and f1 score of 88.1\% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification and structure recognition. 



## keyword



Deep learning, Convolutional neural networksImage processing, Document processing, Table detection, Page object detection, Table classification 



## Getting Started

### Install MMDetection v2.10.0+

TNCR has been implemented and tested with Python 3.7 and PyTorch 1.8.1. 

```

%cd $project_dir$

!pip install -q mmcv terminaltables

!git clone 'https://github.com/open-mmlab/mmdetection.git'

!pip install -r "$project_dir$/mmdetection/requirements/optional.txt"

%cd mmdetection/

!python setup.py install

!python setup.py develop

!pip install -r {"$project_dir$/mmdetection/requirements.txt"}

!pip install pillow

!pip install mmcv

!pip install mmcv-full

%cd ..

!pip uninstall pycocotools

!pip uninstall mmpycocotools

!pip install mmpycocotools

```



## requirements

```

Python: 3.7 

PyTorch: 1.8.1

OpenCV: 4.5.2

MMCV: 1.3.5

MMDetection: v2.10.0

```



##  TNCR Dataset



You can download the dataset through this  link 

or from Google Drive divide by 5 parts 

* Part #1 

* Part #2 

* Part #3 

* Part #4 

* Part #5 



      Full Lined

      



        Merged Cells

             



        No lines

          



        Partial Lined

        

        



  Partial Lined Merged Cells

        

        



##  Models Zoo

All config  and checkpoint files available in this link 



Checkout our demo notebook for loading checkpoints and performing inference 
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lI0ghISktOYkEpaxDEsnEyQ2botA34oB)


#### 1. Cascade Mask R-CNN



  

   BackbonesConfig FilesCheckpoint File

  

  

     Resnet-50_1x    Config Files   Checkpoint     

  

  

     Resnet-50_20e      Config Files   Checkpoint    

  

   

     Resnet-101_1x     Config Files   Checkpoint 

  

   

     Resnet-101_20e     Config Files   Checkpoint 

  

   

    ResNeXt-101-32x4d_1x      Config Files   Checkpoint 

  

   

     ResNeXt-101-64x4d_1x     Config Files   Checkpoint 

  



#### 2. Cascade R-CNN



  

   BackbonesConfig FilesCheckpoint File

  

  

     Resnet-50_1x    Config Files   Checkpoint 

  

  

     Resnet-50_20e     Config Files   Checkpoint 

  

   

     Resnet-101_1x   Config Files   Checkpoint   

  

   

     Resnet-101_20e     Config Files   Checkpoint 

  

   

    ResNeXt-101-32x4d_1x      Config Files   Checkpoint 

  

   

     ResNeXt-101-64x4d_1x     Config Files   Checkpoint 

  



#### 3. Cascade RPN



  

    Method BackbonesConfig FilesCheckpoint File

  

  

   Fast R-CNN    Resnet-50_1x   Config Files   Checkpoint 

  

   

    CRPN   Resnet-50_1x     Config Files   Checkpoint 

  

  



#### 4. Hybrid Task Cascade



  

   BackbonesConfig FilesCheckpoint File

  

  

     Resnet-50_1x   Config Files   Checkpoint 

  

   

     Resnet-50_20e     Config Files   Checkpoint 

  

   

    Resnet-101_1x      Config Files   Checkpoint 

  

   



#### 5. YOLO



  

   BackbonesConfig FilesCheckpoint File

  

  

    DarkNet-53_320   Config Files   Checkpoint 

  

  

      DarkNet-53_416    Config Files   Checkpoint 

  

   

      DarkNet-53_608    Config Files   Checkpoint 

  



#### 5. Deformable DERT



  

   BackbonesConfig FilesCheckpoint File

  

  

    R-50_1   Config Files   Checkpoint 

  

  



## License

The code of TNCR is Open Source under the [MIT License](LICENSE). There is no limitation for both acadmic and commercial usage.



## Cite as

If you find this work useful for your research, please cite our paper:

```

@misc{https://doi.org/10.48550/arxiv.2211.08469,

  doi = {10.48550/ARXIV.2211.08469},

  url = {https://arxiv.org/abs/2211.08469},

  author = {Kasem, Mahmoud and Abdallah, Abdelrahman and Berendeyev, Alexander and Elkady, Ebrahem and Abdalla, Mahmoud and Mahmoud, Mohamed and Hamada, Mohamed and Nurseitov, Daniyar and Taj-Eddin, Islam},

  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},

  title = {Deep learning for table detection and structure recognition: A survey},

  publisher = {arXiv},

  year = {2022},

  copyright = {Creative Commons Attribution 4.0 International}

}



```

```

@article{ABDALLAH2021,

title = {TNCR: Table Net Detection and Classification Dataset},

journal = {Neurocomputing},

year = {2021},

issn = {0925-2312},

doi = {https://doi.org/10.1016/j.neucom.2021.11.101},

url = {https://www.sciencedirect.com/science/article/pii/S0925231221018142},

author = {Abdelrahman Abdallah and Alexander Berendeyev and Islam Nuradin and Daniyar Nurseitov},

keywords = {Deep learning, Convolutional neural networks, Image processing, Document processing, Table detection, Page object detection},

abstract = {We present TNCR, a new table dataset with varying image quality collected from open access websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 labeled tables with approximately 6621 images . In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Deformable DERT with Resnet-50 Backbone Network achieves the highest performance compared to other methods with a precision of 86.7%, recall of 89.6%, and f1 score of 88.1% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification and structure recognition. The dataset and trained model checkpoints are available at https://github.com/abdoelsayed2016/TNCR_Dataset.}

}

```

```