metadata
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 11563543600
num_examples: 16450
- name: test
num_bytes: 1638512366
num_examples: 3159
download_size: 15933142593
dataset_size: 13202055966
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
TabComp 📊
A Benchmark for OCR-Free Visual Table Reading Comprehension
This dataset accompanies the paper TabComp: A Dataset for Visual Table Reading Comprehension
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