# Doc2Table: End-to-End Table Extraction and Challenges with Large Vision-Language Models

> Introduces the Doc2Table project, exploring end-to-end document table extraction using large vision-language models, including challenging benchmark tests and the latest technical solutions.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-02T10:08:58.000Z
- 最近活动: 2026-04-02T10:26:00.253Z
- 热度: 157.7
- 关键词: 表格提取, 视觉语言模型, 文档智能, OCR, 结构化数据, LVLM, 端到端学习
- 页面链接: https://www.zingnex.cn/en/forum/thread/doc2table
- Canonical: https://www.zingnex.cn/forum/thread/doc2table
- Markdown 来源: floors_fallback

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## [Introduction] Doc2Table: Exploring Challenges and Solutions of Large Vision-Language Models in End-to-End Table Extraction

The Doc2Table project focuses on the application of Large Vision-Language Models (LVLM) in end-to-end document table extraction, covering core challenges of table extraction, advantages of LVLM, key components of the project (end-to-end framework, challenging benchmark tests, model comparison), as well as experimental findings and future directions.

## [Background] Difficulties in Table Extraction and New Hope from LVLM

Table extraction has become a document intelligence challenge due to visual diversity (variable borders/layouts), complex layouts (mixed arrangement, cross-page, merged cells), content ambiguity (OCR errors/ambiguity), and structured output requirements; traditional multi-stage pipelines are prone to error cascading and struggle to handle complex tables; LVLM has advantages such as end-to-end reasoning, strong generalization ability, and multi-modal understanding, bringing new possibilities to table extraction.

## [Methodology] Core Components of the Doc2Table Project

Doc2Table consists of three parts: 1. End-to-end extraction framework (directly outputs structured formats like HTML/Markdown from input images); 2. Challenging benchmark dataset (covers simple/complex/borderless/mixed-layout/low-quality tables, evaluating accuracy and structural correctness); 3. Multi-model comparative analysis (commercial/open-source models, evaluating accuracy, robustness, efficiency, and cost).

## [Technical Implementation] Key Technical Details of Doc2Table

1. Prompt engineering: Exploring zero-shot, few-shot, chain-of-thought, and step-by-step prompt strategies to improve extraction quality; 2. Output parsing and validation: Structured parsing of model outputs, consistency checks (e.g., number of cells per row), confidence evaluation; 3. Error recovery and iteration: Local retries, feedback loops, multi-model integration.

## [Experimental Findings] Model Performance and Error Patterns

Experimental findings: 1. Model size is positively correlated with performance but with diminishing returns; complex tables require large commercial models; 2. Domain-pre-trained models outperform general-purpose models; 3. Common errors: Boundary recognition errors, hierarchical relationship confusion, cross-page processing failures, difficulty in handwritten content recognition.

## [Application Scenarios] Practical Application Areas of Doc2Table

Applied in areas such as document digitization (accelerating archive processing), financial statement processing (supporting automated analysis), scientific literature mining (extracting experimental data), and medical record processing (assisting clinical decision-making).

## [Limitations and Future] Current Challenges and Improvement Directions

Current limitations: High computational cost, latency issues, limited support for specialized tables; Future directions: Efficiency optimization (lightweight models/inference optimization), multi-language support, interactive extraction, integration with other document intelligence tasks.

## [Conclusion] Significance and Outlook of Doc2Table

Doc2Table demonstrates the potential of LVLM in table extraction; the end-to-end approach simplifies the process but needs to address cost and latency issues; progress in table extraction will drive multi-domain applications, and we look forward to more efficient and general solutions.