Update README.md

README.md

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   - split: test
     path: data/test-*
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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
+
+Each example:
+
+```json
+{
+  "id": "tabcomp-001",
+  "image": "documents/image1.png",
+  "question": "Who is the investigator at the University of California?",
+  "answer": "The investigator at the University of California is Dr. William W. Parmley."
+}
+
+from datasets import load_dataset
+
+ds = load_dataset("DIALab/TabComp")
+
+print(ds)
+
+# sample
+ex = ds["test"][0]
+print(ex["question"])
+print(ex["answer"])
+
+🏆 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.
+
+@inproceedings{gautam2025tabcomp,
+  title={TabComp: A Dataset for Visual Table Reading Comprehension},
+  author={Gautam, Somraj and Bhandari, Abhishek and Harit, Gaurav},
+  booktitle={Findings of NAACL 2025},
+  year={2025}
+}
+
+We welcome:
+
+New model evaluations
+Error analysis
+Extensions to multilingual / multi-table settings
+
+## Contact
+
+For collaboration, email **Somraj Gautam** gautam.8@iitj.ac.in