# Rethinking Image-based Table Recognition Using Weakly Supervised Methods

arXiv: https://arxiv.org/abs/2303.07641 · HTML: https://mlsanity.com/paper/2303.07641/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context](https://mlsanity.com/paper/2005.00589/) (2020)
- [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://mlsanity.com/paper/2110.00061/) (2021)
- [CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents](https://mlsanity.com/paper/2004.12629/) (2020)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [Image-to-Markup Generation with Coarse-to-Fine Attention](https://mlsanity.com/paper/1609.04938/) (2016)
- [MASTER: Multi-Aspect Non-local Network for Scene Text Recognition](https://mlsanity.com/paper/1910.02562/) (2019)
- [Table Structure Recognition using Top-Down and Bottom-Up Cues](https://mlsanity.com/paper/2010.04565/) (2020)
- [LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment](https://mlsanity.com/paper/2105.06224/) (2021)
- [Split, embed and merge: An accurate table structure recognizer](https://mlsanity.com/paper/2107.05214/) (2021)
- [PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML](https://mlsanity.com/paper/2105.01848/) (2021)
- [ICDAR 2021 Competition on Scientific Literature Parsing](https://mlsanity.com/paper/2106.14616/) (2021)
- [ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX](https://mlsanity.com/paper/2105.14426/) (2021)
- [The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images](https://mlsanity.com/paper/1912.05846/) (2019)

## Built on by (most cited first, 10 of 10)

- [SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion](https://mlsanity.com/paper/2503.11576/) (2025)
- [Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction](https://mlsanity.com/paper/2410.21169/) (2024)
- [TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content](https://mlsanity.com/paper/2404.10305/) (2024)
- [Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition](https://mlsanity.com/paper/2404.13268/) (2024)
- [Synthesizing Realistic Data for Table Recognition](https://mlsanity.com/paper/2404.11100/) (2024)
- [Spatial ModernBERT: Spatial-Aware Transformer for Table and Key-Value Extraction in Financial Documents at Scale](https://mlsanity.com/paper/2507.08865/) (2025)
- [TabIQA: Table Questions Answering on Business Document Images](https://mlsanity.com/paper/2303.14935/) (2023)
- [Hierarchical Modeling Approach to Fast and Accurate Table Recognition](https://mlsanity.com/paper/2512.21083/) (2025)
- [Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations](https://mlsanity.com/paper/2606.17874/) (2026)
- [DocPO: Advancing Document Policy Optimization via Tailored Step-Aware Rewards](https://mlsanity.com/paper/2608.00536/) (2026)

## Abstract (arXiv)

Most of the previous methods for table recognition rely on training datasets containing many richly annotated table images. Detailed table image annotation, e.g., cell or text bounding box annotation, however, is costly and often subjective. In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or LaTeX) code-level annotations of table images. The proposed model consists of three main parts: an encoder for feature extraction, a structure decoder for generating table structure, and a cell decoder for predicting the content of each cell in the table. Our system is trained end-to-end by stochastic gradient descent algorithms, requiring only table images and their ground-truth HTML (or LaTeX) representations. To facilitate table recognition with deep learning, we create and release WikiTableSet, the largest publicly available image-based table recognition dataset built from Wikipedia. WikiTableSet contains nearly 4 million English table images, 590K Japanese table images, and 640k French table images with corresponding HTML representation and cell bounding boxes. The extensive experiments on WikiTableSet and two large-scale datasets: FinTabNet and PubTabNet demonstrate that the proposed weakly supervised model achieves better, or similar accuracies compared to the state-of-the-art models on all benchmark datasets.