## BUDDI Table Factory: A toolbox for generating synthetic documents with annotated tables and cells


**About**

In table detection, we initialize the weights with a pre-trained CDeCNet model using COCO dataset. We re-train the model for five epochs using a stochastic gradient descent optimizer with a learning rate of 0.00125, the momentum of 0.9, and weight decay of 0.0001.

***Hardware Used***

We perform all the experiments on NVIDIA GeForce RTX 2080 Ti GPU with 12 GB GPU memory, Intel(R) Xeon(R) CPU E5-2640 v2 @ 2.00GHz, and 128 GB of RAM.

**Table Detection Model & Training Parameter**
***Optimizer*** 
| Parameter |Value  |
|--|--|
| Type | SGD |
| Learning Rate |0.00125  |
| Momentum | 0.8 |
| Weight Decay |0.001  |


*** Learning Policy ***
| Parameter |Value  |
|--|--|
| Policy | Step |
|Warmup | Linear  |
| Warmup Iteration | 100 |
| Warmup Ratio |0.001  |
| Step  | 4,16,32  |

***General Parameter***
| Parameter |Value  |
|--|--|
| Epoch | 5 |
| Step Interval |50  |
        
***Model Paper Reference***

CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images

https://arxiv.org/abs/2008.10831