| --- |
| dataset_info: |
| - config_name: mmt |
| features: |
| - name: id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: topic |
| dtype: string |
| - name: State/UT |
| dtype: string |
| - name: English |
| dtype: string |
| - name: Hindi |
| dtype: string |
| - name: Bengali |
| dtype: string |
| - name: Gujarati |
| dtype: string |
| - name: Kannada |
| dtype: string |
| - name: Malayalam |
| dtype: string |
| - name: Marathi |
| dtype: string |
| - name: Odia |
| dtype: string |
| - name: Punjabi |
| dtype: string |
| - name: Tamil |
| dtype: string |
| - name: Telugu |
| dtype: string |
| - name: source_url |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 14424797 |
| num_examples: 106 |
| download_size: 13255747 |
| dataset_size: 14424797 |
| - config_name: ocr |
| features: |
| - name: id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: text |
| dtype: string |
| - name: language |
| dtype: string |
| - name: page_url |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 614014454 |
| num_examples: 876 |
| download_size: 612223184 |
| dataset_size: 614014454 |
| - config_name: vqa_en |
| features: |
| - name: id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: topic |
| dtype: string |
| - name: State/UT |
| dtype: string |
| - name: language |
| dtype: string |
| - name: short_q1 |
| dtype: string |
| - name: short_a1 |
| dtype: string |
| - name: short_q2 |
| dtype: string |
| - name: short_a2 |
| dtype: string |
| - name: mcq |
| dtype: string |
| - name: mcq_a |
| dtype: string |
| - name: mcq_opt1 |
| dtype: string |
| - name: mcq_opt2 |
| dtype: string |
| - name: mcq_opt3 |
| dtype: string |
| - name: mcq_opt4 |
| dtype: string |
| - name: true_false_q |
| dtype: string |
| - name: true_false_a |
| dtype: string |
| - name: long_q |
| dtype: string |
| - name: long_a |
| dtype: string |
| - name: adversarial_question |
| dtype: string |
| - name: adversarial_answer |
| dtype: string |
| - name: source_url |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 1131332865 |
| num_examples: 4117 |
| download_size: 1127187152 |
| dataset_size: 1131332865 |
| - config_name: vqa_indic |
| features: |
| - name: id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: topic |
| dtype: string |
| - name: State/UT |
| dtype: string |
| - name: language |
| dtype: string |
| - name: short_q1 |
| dtype: string |
| - name: short_a1 |
| dtype: string |
| - name: short_q2 |
| dtype: string |
| - name: short_a2 |
| dtype: string |
| - name: mcq |
| dtype: string |
| - name: mcq_a |
| dtype: string |
| - name: mcq_opt1 |
| dtype: string |
| - name: mcq_opt2 |
| dtype: string |
| - name: mcq_opt3 |
| dtype: string |
| - name: mcq_opt4 |
| dtype: string |
| - name: true_false_q |
| dtype: string |
| - name: true_false_a |
| dtype: string |
| - name: long_q |
| dtype: string |
| - name: long_a |
| dtype: string |
| - name: adversarial_question |
| dtype: string |
| - name: adversarial_answer |
| dtype: string |
| - name: source_url |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 276711951 |
| num_examples: 1007 |
| download_size: 273419974 |
| dataset_size: 276711951 |
| - config_name: vqa_parallel |
| features: |
| - name: id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: topic |
| dtype: string |
| - name: State/UT |
| dtype: string |
| - name: language |
| dtype: string |
| - name: short_q1 |
| dtype: string |
| - name: short_a1 |
| dtype: string |
| - name: short_q2 |
| dtype: string |
| - name: short_a2 |
| dtype: string |
| - name: mcq |
| dtype: string |
| - name: mcq_a |
| dtype: string |
| - name: mcq_opt1 |
| dtype: string |
| - name: mcq_opt2 |
| dtype: string |
| - name: mcq_opt3 |
| dtype: string |
| - name: mcq_opt4 |
| dtype: string |
| - name: true_false_q |
| dtype: string |
| - name: true_false_a |
| dtype: string |
| - name: long_q |
| dtype: string |
| - name: long_a |
| dtype: string |
| - name: adversarial_question |
| dtype: string |
| - name: adversarial_answer |
| dtype: string |
| - name: source_url |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 324650384 |
| num_examples: 1166 |
| download_size: 321701661 |
| dataset_size: 324650384 |
| 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 |
|
|
| [](https://openreview.net/forum?id=LmJoLn04iL) |
| [](https://arxiv.org/abs/2511.04727) |
| [](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} |
| } |
| ``` |
