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AtharvImmverse
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IndicVisionBench

Tasks:
Visual Question Answering
Modalities:
Image
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Formats:
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Languages:
English
Hindi
Tamil
Size:
1K - 10K
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vision
ocr
vqa
indic
benchmark
cultural
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IndicVisionBench / README.md
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metadata 
dataset_info:
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configs:
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    data_files:
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        path: mmt/test-*
  - config_name: ocr
    data_files:
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        path: ocr/test-*
  - config_name: vqa_en
    data_files:
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  - config_name: vqa_indic
    data_files:
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        path: vqa_indic/test-*
  - config_name: vqa_parallel
    data_files:
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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 arXiv IndicVisionBench-Github

This repository contains the dataset for IndicVisionBench, introduced in

“IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs”
📄 arXiv: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:

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

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:

@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}
}