https://github.com/jainammm/TableNet

Unofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"
https://github.com/jainammm/TableNet

Last synced: 9 days ago
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Unofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"

• Host: GitHub
• URL: https://github.com/jainammm/TableNet
• Owner: jainammm
• Created: 2020-07-11T18:08:35.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-03-25T00:56:17.000Z (over 1 year ago)
• Last Synced: 2024-08-02T11:14:43.740Z (3 months ago)
• Language: Jupyter Notebook
• Size: 2.55 MB
• Stars: 314
• Watchers: 4
• Forks: 72
• Open Issues: 14
• Metadata Files:
  • Readme: README.md

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README

        
# TableNet

Unofficial implementation of ICDAR 2019 paper : _TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images._ 

[__Paper__](https://arxiv.org/abs/2001.01469)

## Overview

**Paper: TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images**

TableNet is a modern deep learning architecture that was proposed by a team from TCS Research year in the year 2019. The main motivation was to extract information from scanned tables through mobile phones or cameras.

They proposed a solution that includes accurate detection of the tabular region within an image and subsequently detecting and extracting information from the rows and columns of the detected table.

**Architecture:** The architecture is based out of Long et al., an encoder-decoder model for semantic segmentation. The same encoder/decoder network is used as the FCN architecture for table extraction. The images are preprocessed and modified using the Tesseract OCR. 

Source: [Nanonets](https://nanonets.com/blog/table-extraction-deep-learning/#tablenet?&utm_source=nanonets.com/blog/&utm_medium=blog&utm_content=Table%20Detection,%20Information%20Extraction%20and%20Structuring%20using%20Deep%20Learning)

![architecture](https://github.com/jainammm/TableNet/raw/master/architecture.png)

## How to run

```

pip install -r requirements.txt

```

1. Download the Marmot Dataset from the link given in readme.

1. Run `data_preprocess/generate_mask.py` to generate Table and Column Mask of corresponding images.

1. Follow the `TableNet.ipynb` notebook to train and test the model.

## Challenges

* Require a very decent System with a good GPU for accurate result on High pixel images. 

## Dataset

Download the dataset provided in paper : [Marmot Dataset](https://drive.google.com/drive/folders/1QZiv5RKe3xlOBdTzuTVuYRxixemVIODp).