TableNet-pytorch
Pytorch Implementation of TableNet Research Paper : https://arxiv.org/abs/2001.01469
Description
In this project we will implement an end-to-end Deep learning architecture which will not only localize the Table in an image, but will also generate structure of Table by segmenting columns in that Table. After detecting Table structure from the image, we will use Pytesseract OCR package to read the contents of the Table.
To know more about the approach, refer my medium blog post,
Data
We will use both Marmot and Marmot Extended dataset for Table Recognition. Marmot dataset contains Table bounding box coordinates and extended version of this dataset contains Column bounding box coordinates.
Marmot Dataset : https://www.icst.pku.edu.cn/cpdp/docs/20190424190300041510.zip Marmot Extended dataset : https://drive.google.com/drive/folders/1QZiv5RKe3xlOBdTzuTVuYRxixemVIODp
Download processed Marmot dataset: https://drive.google.com/file/d/1irIm19B58-o92IbD9b5qd6k3F31pqp1o/view?usp=sharing
Model
We will use DenseNet121 as encoder and build model upon it.
Trainable Params
Download saved model : https://drive.google.com/file/d/1TKALmlwUM_n4gULh6A6Q35VPRUpWDmJZ/view?usp=sharing
Performance compared to other encoder models ( Resnet18, EfficientNet-B0, EfficientNet-B1, VGG19 )
Table Detection - F1
Table Detection - Loss
Column Detection - F1
Column Detection - Loss
Predictions
Predictions from the model
After fixing table mask using contours
After fixing column mask using contours
After processing it through pytesseract
Deployed application
Future Work
- [ ] Deploy this application on a remote server using AWS /StreamLit sharing/heroku.
- [ ] Model Quantization for faster inference time.
- [ ] Train for more epochs and compare the performances.
- [ ] Increase data size by adding data from ICDAR 2013 Table recognition dataset.