# The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images

arXiv: https://arxiv.org/abs/1912.05846 · HTML: https://mlsanity.com/paper/1912.05846/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks](https://mlsanity.com/paper/2104.14272/) (2021)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [An End-to-End Multi-Task Learning Model for Image-based Table Recognition](https://mlsanity.com/paper/2303.08648/) (2023)
- [Rethinking Image-based Table Recognition Using Weakly Supervised Methods](https://mlsanity.com/paper/2303.07641/) (2023)
- [TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content](https://mlsanity.com/paper/2404.10305/) (2024)
- [EMBLEM: Enhancing Multi-script Table Detection through Masking](https://mlsanity.com/paper/2609.08330/) (2026)

## Abstract (arXiv)

A correct localisation of tables in a document is instrumental for determining their structure and extracting their contents; therefore, table detection is a key step in table understanding. Nowadays, the most successful methods for table detection in document images employ deep learning algorithms; and, particularly, a technique known as fine-tuning. In this context, such a technique exports the knowledge acquired to detect objects in natural images to detect tables in document images. However, there is only a vague relation between natural and document images, and fine-tuning works better when there is a close relation between the source and target task. In this paper, we show that it is more beneficial to employ fine-tuning from a closer domain. To this aim, we train different object detection algorithms (namely, Mask R-CNN, RetinaNet, SSD and YOLO) using the TableBank dataset (a dataset of images of academic documents designed for table detection and recognition), and fine-tune them for several heterogeneous table detection datasets. Using this approach, we considerably improve the accuracy of the detection models fine-tuned from natural images (in mean a 17%, and, in the best case, up to a 60%).