# TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words

arXiv: https://arxiv.org/abs/2203.06873 · HTML: https://mlsanity.com/paper/2203.06873/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [Complicated Table Structure Recognition](https://mlsanity.com/paper/1908.04729/) (2019)
- [TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images](https://mlsanity.com/paper/2001.01469/) (2020)
- [Rethinking Table Recognition using Graph Neural Networks](https://mlsanity.com/paper/1905.13391/) (2019)

## Built on by (most cited first, 3 of 3)

- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [A Study on Reproducibility and Replicability of Table Structure Recognition Methods](https://mlsanity.com/paper/2304.10439/) (2023)
- [TableSeq: Unified Generation of Structure, Content, and Layout](https://mlsanity.com/paper/2604.16070/) (2026)

## Abstract (arXiv)

Existing methods for Table Structure Recognition (TSR) from camera-captured or scanned documents perform poorly on complex tables consisting of nested rows / columns, multi-line texts and missing cell data. This is because current data-driven methods work by simply training deep models on large volumes of data and fail to generalize when an unseen table structure is encountered. In this paper, we propose to train a deep network to capture the spatial associations between different word pairs present in the table image for unravelling the table structure. We present an end-to-end pipeline, named TSR-DSAW: TSR via Deep Spatial Association of Words, which outputs a digital representation of a table image in a structured format such as HTML. Given a table image as input, the proposed method begins with the detection of all the words present in the image using a text-detection network like CRAFT which is followed by the generation of word-pairs using dynamic programming. These word-pairs are highlighted in individual images and subsequently, fed into a DenseNet-121 classifier trained to capture spatial associations such as same-row, same-column, same-cell or none. Finally, we perform post-processing on the classifier output to generate the table structure in HTML format. We evaluate our TSR-DSAW pipeline on two public table-image datasets -- PubTabNet and ICDAR 2013, and demonstrate improvement over previous methods such as TableNet and DeepDeSRT.