Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer

TL;DR

This paper introduces a novel end-to-end semi-supervised table detection method using deformable transformers, significantly reducing label requirements and outperforming previous CNN-based approaches on multiple datasets.

Contribution

The paper proposes the first end-to-end semi-supervised table detection approach with deformable transformers, improving performance with less labeled data.

Findings

Outperforms previous semi-supervised methods by +1.8 to +3.4 points.

Achieves superior results on PubLayNet, DocBank, ICADR-19, and TableBank datasets.

Reduces label dependency in table detection tasks.

Abstract

Table detection is the task of classifying and localizing table objects within document images. With the recent development in deep learning methods, we observe remarkable success in table detection. However, a significant amount of labeled data is required to train these models effectively. Many semi-supervised approaches are introduced to mitigate the need for a substantial amount of label data. These approaches use CNN-based detectors that rely on anchor proposals and post-processing stages such as NMS. To tackle these limitations, this paper presents a novel end-to-end semi-supervised table detection method that employs the deformable transformer for detecting table objects. We evaluate our semi-supervised method on PubLayNet, DocBank, ICADR-19 and TableBank datasets, and it achieves superior performance compared to previous methods. It outperforms the fully supervised method (Deformable transformer) by +3.4 points on 10\% labels of TableBank-both dataset and the previous CNN-based semi-supervised approach (Soft Teacher) by +1.8 points on 10\% labels of PubLayNet dataset. We hope this work opens new possibilities towards semi-supervised and unsupervised table detection methods.