---
dataset_info:
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    dtype: string
  - name: image
    dtype: image
  - name: topic
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  - name: Hindi
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  - name: Bengali
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  - name: Gujarati
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  - name: Kannada
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  - name: Malayalam
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  - name: Marathi
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  - name: Odia
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  - name: Punjabi
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  - name: Tamil
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  - name: Telugu
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  - name: source_url
    dtype: string
  splits:
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  features:
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    dtype: string
  - name: image
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  - name: text
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  - name: language
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  - name: short_q1
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  - name: short_a1
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  - name: short_a2
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  - name: mcq
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- config_name: vqa_indic
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  - name: image
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  features:
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    dtype: string
  splits:
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    num_bytes: 324650384
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configs:
- config_name: mmt
  data_files:
  - split: test
    path: mmt/test-*
- config_name: ocr
  data_files:
  - split: test
    path: ocr/test-*
- config_name: vqa_en
  data_files:
  - split: test
    path: vqa_en/test-*
- config_name: vqa_indic
  data_files:
  - split: test
    path: vqa_indic/test-*
- config_name: vqa_parallel
  data_files:
  - split: test
    path: vqa_parallel/test-*
task_categories:
- visual-question-answering
language:
- en
- hi
- ta
- te
- ml
- mr
- gu
- pa
- or
- kn
- bn
tags:
- vision
- ocr
- vqa
- indic
- benchmark
- cultural
- mmt
- multimodal
size_categories:
- 10K<n<100K
---


# IndicVisionBench

[![ICLR 2026](https://img.shields.io/badge/ICLR-2026-blue)](https://openreview.net/forum?id=LmJoLn04iL)
[![arXiv](https://img.shields.io/badge/arXiv-2511.04727-b31b1b.svg)](https://arxiv.org/abs/2511.04727)
[![IndicVisionBench-Github](https://img.shields.io/badge/Github-IndicVisionBench-green?logo=github)](https://github.com/ola-krutrim/IndicVisionBench)

This repository contains the dataset for **IndicVisionBench**, introduced in  

**“IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs”**  
📄 [arXiv:2511.04727](https://arxiv.org/abs/2511.04727)  
🏛️ Accepted at **ICLR 2026**  
🔗 OpenReview: https://openreview.net/forum?id=LmJoLn04iL

IndicVisionBench is a **culturally grounded, multilingual vision-language benchmark** designed to evaluate Vision–Language Models (VLMs) on visual understanding tasks in the Indian context. The benchmark focuses on:

- Multilingual Visual Question Answering (VQA)
- Culturally-aware reasoning
- Adversarial robustness
- Parallel cross-lingual consistency
- Optical Character Recognition (OCR) in Indic scripts
- Multimodal Machine Translation (MMT)

Unlike generic VQA datasets, IndicVisionBench emphasizes **Indian cultural context, regional diversity, and Indic language coverage**, enabling systematic evaluation of multilingual and culturally-aware VLMs.

---

## Languages Covered

- English
- Hindi
- Tamil
- Telugu
- Malayalam
- Marathi
- Gujarati
- Punjabi
- Odia
- Kannada
- Bengali

---

## Benchmark Overview

IndicVisionBench consists of five main configurations:

| Config | Task | #Images | Description |
|--------|------|-----------|-------------|
| `mmt` | Multimodal Machine Translation | 106 | Image-grounded translations across Indic languages |
| `ocr` | Optical Character Recognition | 876 | OCR in multiple Indic scripts |
| `vqa_en` | Visual Question Answering | 4,117 | Culturally grounded VQA in English |
| `vqa_indic` | Visual Question Answering | 1,007 | Culturally grounded VQA in Indic languages |
| `vqa_parallel` | Visual Question Answering | 1,166 | Same QA pairs across multiple languages for cross-lingual consistency |

- **Total images across all configs:** 4993
- **Total questions across VQA En, Indic and Parallel:** (4117 + 1007 + 1166)*6 = 37,740

---

## Subset Descriptions

### 1️⃣ Multimodal Machine Translation (`mmt`)

Image-grounded translation benchmark with aligned captions across multiple Indic languages.

**Features:**
- `image`
- `topic`
- `State/UT`
- Parallel captions in 11 languages
- `source_url`

This subset evaluates:
- Cultural terminology consistency
- Visual grounding in translation

### 2️⃣ Optical Character Recognition (`ocr`)

OCR dataset consisting of scanned pages in Indic scripts from Wikisource.

**Features:**
- `image`
- `text`
- `language`
- `page_url`

This subset evaluates OCR capabitilies on Indic scripts/languages.

### 3️⃣ English VQA (`vqa_en`)

Culturally grounded VQA in English.

Each example includes:

- 2 short-answer questions
- 1 multiple-choice question (4 options)
- 1 true/false question
- 1 long-form reasoning question
- 1 adversarial question
- Metadata: `topic`, `language`, `State/UT`, 'source_url'

This subset evaluates:
- Object & scene understanding
- Cultural knowledge
- Fine-grained attribute recognition
- Robustness to false assumptions in the adversarial questions

### 4️⃣ Indic VQA (`vqa_indic`)

Same VQA format as in `vqa_en`, but in Indic languages.

This subset evaluates:
- Multilingual reasoning
- Cultural alignment in local languages

### 5️⃣ Parallel VQA (`vqa_parallel`)

Same VQA format as in `vqa_en`. Parallel multilingual QA pairs for the same image.

This subset enables the study of
- cross-lingual performance of VLMs across 11 languages (English and 10 Indic languages)
- region-specific strengths or biases

## Usage

All configurations can be loaded using `datasets`:

```python
from datasets import load_dataset

# Example: load English VQA split
ds = load_dataset("krutrim-ai-labs/IndicVisionBench", "vqa_en")["test"]

print(ds[0])
```

The following five configurations/splits are present in the dataset: 
- mmt
- ocr
- vqa_en
- vqa_indic
- vqa_parallel

Images are stored directly within the dataset and loaded automatically by 🤗 Datasets.

## Evaluation Dimensions

IndicVisionBench is designed to measure:
- Scene & contextual understanding
- Attribute detection
- Cultural understanding
- Bias & adversarial robustness
- Cross-lingual consistency
- OCR performance
- Image-grounded translation capability

## Code & Evaluation

The official inference and evaluation codebase for IndicVisionBench is available on GitHub.

**GitHub Repository:**
[https://github.com/ola-krutrim/IndicVisionBench](https://github.com/ola-krutrim/IndicVisionBench)

The repository provides the complete pipeline for running inference and reproducing benchmark results across all evaluation tracks.

The codebase includes:

- End-to-end inference pipelines for **Vision-Language Models (VLMs)** and **OCR systems**
- Modular wrappers enabling easy integration of **API-based models** and **open-source models**
- Evaluation pipelines for all benchmark tasks:
  - **OCR evaluation**
  - **Visual Question Answering (VQA)**
    - Structured questions (MCQ, True/False)
    - Open-ended questions (short answer, long answer, adversarial)
  - **Multimodal Machine Translation (MMT)**
- **LLM-as-a-judge evaluation** for open-ended VQA responses
- Data generation scripts for constructing a similar multimodal benchmark.


### Citation

If you use this dataset, please cite:

```bibtex
@inproceedings{faraz2026indicvisionbench,
  title={IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs},
  author={Ali Faraz and Akash and Shaharukh Khan and Raja Kolla and Akshat Patidar and Suranjan Goswami and Abhinav Ravi and Chandra Khatri and Shubham Agarwal},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026},
  url={https://openreview.net/forum?id=LmJoLn04iL}
}
```