TNCR: Table Net Detection and Classification Dataset

Abstract

We present TNCR, a new table dataset with varying image quality collected from free websites. 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

KOHTD: Kazakh Offline Handwritten Text Dataset

“…Deep learning has been widely used in several fields nowadays, such as Medical applications like Cancers diagnoses, detection, and classification [1], image captioning [2], speech recognition [3] and in medical question answers [4,5,6], table detection and recognition [7,8] also deep learning has been used in software engineering such as optimizing the time and schedule of the software projects [9,10] and one of the most usages of Deep Learning is handwritten recognition for different languages as we will discuss.…”

Document AI: Benchmarks, Models and Applications

“…PubTabNet(Zhong et al, 2019a) proposes a large-scale table dataset and provides table structure and cell content to assist in table recognition. TNCR(Abdallah et al, 2021) provides the label of the table categories while providing the table boundaries. Many deep learning based object detection models have achieved good results in table detection.…”