# OCR Synthetic Benchmark Dataset for Indic Languages

arXiv: https://arxiv.org/abs/2205.02543 · HTML: https://mlsanity.com/paper/2205.02543/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 3 of 3)

- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis](https://mlsanity.com/paper/2103.15348/) (2021)
- [Benchmarking Scene Text Recognition in Devanagari, Telugu and Malayalam](https://mlsanity.com/paper/2104.04437/) (2021)

## Built on by (most cited first, 4 of 4)

- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)
- [PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Language](https://mlsanity.com/paper/2505.10055/) (2025)
- [VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extraction](https://mlsanity.com/paper/2510.10490/) (2025)
- [KazakhOCR: A Synthetic Benchmark for Evaluating Multimodal Models in Low-Resource Kazakh Script OCR](https://mlsanity.com/paper/2603.13238/) (2026)

## Abstract (arXiv)

We present the largest publicly available synthetic OCR benchmark dataset for Indic languages. The collection contains a total of 90k images and their ground truth for 23 Indic languages. OCR model validation in Indic languages require a good amount of diverse data to be processed in order to create a robust and reliable model. Generating such a huge amount of data would be difficult otherwise but with synthetic data, it becomes far easier. It can be of great importance to fields like Computer Vision or Image Processing where once an initial synthetic data is developed, model creation becomes easier. Generating synthetic data comes with the flexibility to adjust its nature and environment as and when required in order to improve the performance of the model. Accuracy for labeled real-time data is sometimes quite expensive while accuracy for synthetic data can be easily achieved with a good score.