  We present TNCR, a new table dataset with varying image quality collected
from free websites. The TNCR dataset can be used for table detection in scanned
document images and their classification into 5 different classes. TNCR
contains 9428 high-quality labeled images. In this paper, we have implemented
state-of-the-art deep learning-based methods for table detection to create
several strong baselines. Cascade Mask R-CNN with ResNeXt-101-64x4d Backbone
Network achieves the highest performance compared to other methods with a
precision of 79.7%, recall of 89.8%, and f1 score of 84.4% 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.
