OCR Synthetic Benchmark Dataset for Indic Languages
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 5 May 2022.
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3 of 10 references.
References · 3
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Languagecited by 0
- 2025VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extractioncited by 0
- 2026KazakhOCR: A Synthetic Benchmark for Evaluating Multimodal Models in Low-Resource Kazakh Script OCRcited by 0
- 2026KazakhOCR: A Synthetic Benchmark for Evaluating Multimodal Models in Low-Resource Kazakh Script OCR17 Feb 2026
- 2025VOLTAGE: A Versatile Contrastive Learning based OCR Methodology for ultra low-resource scripts through Auto Glyph Feature Extraction12 Oct 2025
- 2025PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Language15 May 2025