# Deciphering the Underserved: Benchmarking LLM OCR for Low-Resource Scripts

arXiv: https://arxiv.org/abs/2412.16119 · HTML: https://mlsanity.com/paper/2412.16119/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 20 Dec 2024.

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

- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition](https://mlsanity.com/paper/1406.2227/) (2014)
- [Optical Text Recognition in Nepali and Bengali: A Transformer-based Approach](https://mlsanity.com/paper/2404.02375/) (2024)

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

- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities](https://mlsanity.com/paper/2510.06743/) (2025)
- [OmniOCR: Generalist OCR for Ethnic Minority Languages](https://mlsanity.com/paper/2602.21042/) (2026)
- [BanglaWild: An In-the-Wild Bengali Scene Text Recognition Benchmark for OCR and Vision-Language Models](https://mlsanity.com/paper/2608.03884/) (2026)

## Abstract (arXiv)

This study investigates the potential of Large Language Models (LLMs), particularly GPT-4o, for Optical Character Recognition (OCR) in low-resource scripts such as Urdu, Albanian, and Tajik, with English serving as a benchmark. Using a meticulously curated dataset of 2,520 images incorporating controlled variations in text length, font size, background color, and blur, the research simulates diverse real-world challenges. Results emphasize the limitations of zero-shot LLM-based OCR, particularly for linguistically complex scripts, highlighting the need for annotated datasets and fine-tuned models. This work underscores the urgency of addressing accessibility gaps in text digitization, paving the way for inclusive and robust OCR solutions for underserved languages.