# From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing

arXiv: https://arxiv.org/abs/2608.09842 · HTML: https://mlsanity.com/paper/2608.09842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 27 of 27)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning](https://mlsanity.com/paper/2501.00321/) (2024)
- [OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations](https://mlsanity.com/paper/2412.07626/) (2024)
- [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)
- [MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing](https://mlsanity.com/paper/2509.22186/) (2025)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy](https://mlsanity.com/paper/2412.02210/) (2024)
- [TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy](https://mlsanity.com/paper/2406.01326/) (2024)
- [Split, embed and merge: An accurate table structure recognizer](https://mlsanity.com/paper/2107.05214/) (2021)
- [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)
- [TSRFormer: Table Structure Recognition with Transformers](https://mlsanity.com/paper/2208.04921/) (2022)
- [MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale](https://mlsanity.com/paper/2604.04771/) (2026)
- [TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers](https://mlsanity.com/paper/2208.14687/) (2022)
- [LORE: Logical Location Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2303.03730/) (2023)
- [Visual Understanding of Complex Table Structures from Document Images](https://mlsanity.com/paper/2111.07129/) (2021)
- [UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining](https://mlsanity.com/paper/2403.04822/) (2024)
- [Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer](https://mlsanity.com/paper/2303.11615/) (2023)
- [Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR](https://mlsanity.com/paper/2504.11101/) (2025)
- [Real5-OmniDocBench: A Full-Scale Physical Reconstruction Benchmark for Robust Document Parsing in the Wild](https://mlsanity.com/paper/2603.04205/) (2026)
- [UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition](https://mlsanity.com/paper/2409.13148/) (2024)
- [Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation](https://mlsanity.com/paper/2303.09174/) (2023)
- [Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities](https://mlsanity.com/paper/2405.16234/) (2024)
- [Revise: A Framework for Revising OCRed text in Practical Information Systems with Data Contamination Strategy](https://mlsanity.com/paper/2604.08115/) (2026)
- [ParseFixer: An Agentic Framework for Document Parsing via Selective Multimodal Correction](https://mlsanity.com/paper/2606.11977/) (2026)

## Abstract (arXiv)

Recent document parsers achieve table TEDS scores above 93 on OmniDocBench v1.6, yet community feedback and our audit reveal persistent failures on complex real-world tables. To quantify this gap, we introduce TableParseMap, a diagnostic benchmark of 916 real-world tables organized into five challenging scenarios and nine failure types. The strongest evaluated parser achieves only 85.03 TEDS, showing that aggregate benchmark scores conceal substantial weaknesses. Our analysis attributes these failures to three complementary limitations: large tables exceed the reliable processing scale of a single pass, weak or ambiguous visual cues hinder structure perception, and the reconstructed table may remain visually inconsistent with the image. We therefore propose DEC (Decompose--Enhance--Correct), a visual-consistency-guided agentic framework that improves frozen table parsers without retraining. DEC uses a general VLM as the controller: Decompose partitions large tables along structure-aware boundaries, Enhance exposes weak visual evidence and reparses transformed views, and Correct diagnoses and repairs residual errors. A Visual Consistency Gate (VC-Gate) selectively triggers intervention, while a Visual Consistency Ranker (VC-Ranker) verifies candidate updates and supports rollback without ground-truth HTML at inference time. We further derive a 1,977-table Consensus-Hard Set from 4,556 candidates through offline metrics and cross-model consensus. Across three frozen parsers, DEC improves TEDS by 1.57 points on average; on TableParseMap, gains reach 1.89 points overall, 2.62 on structural errors, and 5.66 on large tables.