---
dataset_info:
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  - name: answer
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  - name: test
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configs:
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    path: data/train-*
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---

# TabComp 📊  
**A Benchmark for OCR-Free Visual Table Reading Comprehension**

This dataset accompanies the paper [TabComp: A Dataset for Visual Table Reading Comprehension](https://aclanthology.org/2025.findings-naacl.320.pdf)

TabComp evaluates **Vision-Language Models (VLMs)** on their ability to **read, understand, and reason over table images** without relying on OCR, using **generative question answering**.

## 🔍 Why TabComp?
Modern VLMs perform well on general VQA but struggle with **tables**, which require:
- Structured reasoning across rows/columns  
- Understanding layout + text jointly  
- Multi-step inference over semi-structured data  

👉 TabComp isolates this challenge and provides a **focused benchmark for table understanding**.
## 📊 Dataset Overview

- **Images:** 3,318 table images  
- **QA pairs:** 19,610  
- **Answer type:** Generative (natural language)  
- **Domain:** Industrial documents  
- **Text types:** Printed + handwritten  

### Task Definition

Given:
- A **table image**
- A **question**

Generate:
- A **natural language answer** requiring table comprehension
## 🧠 What Makes It Challenging?
- ❌ No OCR signals  
- ✅ Dense textual + structural information  
- ✅ Long-range dependencies across table cells  
- ✅ Generative answers (not extractive spans)  
## 📁 Data Format

🏆 Leaderboard (Baseline Results)
Performance on TabComp (generative metrics):
| Model       | Setting    | B-4 ↑     | ROUGE-L ↑ | BERTScore ↑ | METEOR ↑  |
| ----------- | ---------- | --------- | --------- | ----------- | --------- |
| Donut-base  | Fine-tuned | **42.69** | 37.29     | 83.38       | **60.14** |
| Donut-base  | End-to-end | 28.59     | 32.24     | 85.06       | 47.19     |
| Donut-proto | Fine-tuned | 6.49      | 17.84     | 73.26       | 19.80     |
| Donut-proto | End-to-end | 34.87     | 37.02     | 87.74       | 56.49     |
| UReader     | Zero-shot  | 28.14     | **37.64** | **88.04**   | 20.71     |

Full metrics (BLEU-1/2/3/4, CIDEr) available in the paper.

## We welcome:
- New model evaluations
- Error analysis
- Extensions to multilingual / multi-table settings

## Contact

For collaboration, email **Somraj Gautam** gautam.8@iitj.ac.in