# PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML

arXiv: https://arxiv.org/abs/2105.01848 · HTML: https://mlsanity.com/paper/2105.01848/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 9 of 9)

- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Context Encoding for Semantic Segmentation](https://mlsanity.com/paper/1803.08904/) (2018)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [Shape Robust Text Detection with Progressive Scale Expansion Network](https://mlsanity.com/paper/1903.12473/) (2019)
- [Gradient Centralization: A New Optimization Technique for Deep Neural Networks](https://mlsanity.com/paper/2004.01461/) (2020)
- [MASTER: Multi-Aspect Non-local Network for Scene Text Recognition](https://mlsanity.com/paper/1910.02562/) (2019)
- [Exploiting Deep Representations for Neural Machine Translation](https://mlsanity.com/paper/1810.10181/) (2018)
- [Learning Graph Normalization for Graph Neural Networks](https://mlsanity.com/paper/2009.11746/) (2020)

## Built on by (most cited first, 30 of 39)

- [MinerU: An Open-Source Solution for Precise Document Content Extraction](https://mlsanity.com/paper/2409.18839/) (2024)
- [MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm](https://mlsanity.com/paper/2506.05218/) (2025)
- [OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition](https://mlsanity.com/paper/2403.19128/) (2024)
- [Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation](https://mlsanity.com/paper/2310.16809/) (2023)
- [Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction](https://mlsanity.com/paper/2410.21169/) (2024)
- [Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling](https://mlsanity.com/paper/2303.06949/) (2023)
- [Optimized Table Tokenization for Table Structure Recognition](https://mlsanity.com/paper/2305.03393/) (2023)
- [A large-scale dataset for end-to-end table recognition in the wild](https://mlsanity.com/paper/2303.14884/) (2023)
- [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025)
- [PP-StructureV2: A Stronger Document Analysis System](https://mlsanity.com/paper/2210.05391/) (2022)
- [LORE: Logical Location Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2303.03730/) (2023)
- [An End-to-End Multi-Task Learning Model for Image-based Table Recognition](https://mlsanity.com/paper/2303.08648/) (2023)
- [GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction](https://mlsanity.com/paper/2309.14962/) (2023)
- [UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining](https://mlsanity.com/paper/2403.04822/) (2024)
- [Rethinking Image-based Table Recognition Using Weakly Supervised Methods](https://mlsanity.com/paper/2303.07641/) (2023)
- [TFLOP: Table Structure Recognition Framework with Layout Pointer Mechanism](https://mlsanity.com/paper/2501.11800/) (2025)
- [Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Models](https://mlsanity.com/paper/2509.17589/) (2025)
- [LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training](https://mlsanity.com/paper/2401.01522/) (2024)
- [SPRINT: Script-agnostic Structure Recognition in Tables](https://mlsanity.com/paper/2503.11932/) (2025)
- [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)
- [TABLET: Table Structure Recognition using Encoder-only Transformers](https://mlsanity.com/paper/2506.07015/) (2025)
- [High-Performance Transformers for Table Structure Recognition Need Early Convolutions](https://mlsanity.com/paper/2311.05565/) (2023)
- [Synthesizing Realistic Data for Table Recognition](https://mlsanity.com/paper/2404.11100/) (2024)
- [1st Place Solution for ICDAR 2021 Competition on Mathematical Formula Detection](https://mlsanity.com/paper/2107.05534/) (2021)
- [Rethinking Detection Based Table Structure Recognition for Visually Rich Document Images](https://mlsanity.com/paper/2312.00699/) (2023)
- [PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction](https://mlsanity.com/paper/2409.05125/) (2024)
- [TableCenterNet: A one-stage network for table structure recognition](https://mlsanity.com/paper/2504.17522/) (2025)
- [TDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignment](https://mlsanity.com/paper/2603.22819/) (2026)
- [InstructTable: Improving Table Structure Recognition Through Instructions](https://mlsanity.com/paper/2604.02880/) (2026)

## Abstract (arXiv)

This paper presents our solution for ICDAR 2021 competition on scientific literature parsing taskB: table recognition to HTML. In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment.Our table structure recognition algorithm is customized based on MASTER [1], a robust image textrecognition algorithm. PSENet [2] is used to detect each text line in the table image. For text linerecognition, our model is also built on MASTER. Finally, in the box assignment phase, we associatedthe text boxes detected by PSENet with the structure item reconstructed by table structure prediction,and fill the recognized content of the text line into the corresponding item. Our proposed methodachieves a 96.84% TEDS score on 9,115 validation samples in the development phase, and a 96.32%TEDS score on 9,064 samples in the final evaluation phase.