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# TabComp 📊
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**A Benchmark for OCR-Free Visual Table Reading Comprehension**
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This dataset accompanies the paper [TabComp: A Dataset for Visual Table Reading Comprehension](https://aclanthology.org/2025.findings-naacl.320.pdf)
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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**.
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## 🔍 Why TabComp?
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Modern VLMs perform well on general VQA but struggle with **tables**, which require:
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- Structured reasoning across rows/columns
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- Multi-step inference over semi-structured data
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👉 TabComp isolates this challenge and provides a **focused benchmark for table understanding**.
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## 📊 Dataset Overview
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- **Images:** 3,318 table images
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# TabComp 📊
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**A Benchmark for OCR-Free Visual Table Reading Comprehension**
|
|
|
|
| 32 |
This dataset accompanies the paper [TabComp: A Dataset for Visual Table Reading Comprehension](https://aclanthology.org/2025.findings-naacl.320.pdf)
|
|
|
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| 33 |
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**.
|
| 34 |
|
|
|
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| 35 |
## 🔍 Why TabComp?
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Modern VLMs perform well on general VQA but struggle with **tables**, which require:
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- Structured reasoning across rows/columns
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|
|
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- Multi-step inference over semi-structured data
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| 40 |
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👉 TabComp isolates this challenge and provides a **focused benchmark for table understanding**.
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## 📊 Dataset Overview
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- **Images:** 3,318 table images
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