# OCRTurk: A Comprehensive OCR Benchmark for Turkish

arXiv: https://arxiv.org/abs/2602.03693 · HTML: https://mlsanity.com/paper/2602.03693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Placed in

- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)

## Builds on (most cited first, 10 of 10)

- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning](https://mlsanity.com/paper/2501.00321/) (2024)
- [CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy](https://mlsanity.com/paper/2412.02210/) (2024)
- [Evaluating the Quality of Benchmark Datasets for Low-Resource Languages: A Case Study on Turkish](https://mlsanity.com/paper/2504.09714/) (2025)
- [TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content](https://mlsanity.com/paper/2404.10305/) (2024)
- [Benchmarking Image Similarity Metrics for Novel View Synthesis Applications](https://mlsanity.com/paper/2506.12563/) (2025)
- [STRIDE : Scene Text Recognition In-Device](https://mlsanity.com/paper/2105.07795/) (2021)
- [A Conglomerate of Multiple OCR Table Detection and Extraction](https://mlsanity.com/paper/2010.08591/) (2020)
- [Cosmos-LLaVA: Chatting with the Visual Cosmos-LLaVA: G"orselle Sohbet Etmek](https://mlsanity.com/paper/2412.02760/) (2024)
- [Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices](https://mlsanity.com/paper/2507.07029/) (2025)

## Abstract (arXiv)

Document parsing is now widely used in applications, such as large-scale document digitization, retrieval-augmented generation, and domain-specific pipelines in healthcare and education. Benchmarking these models is crucial for assessing their reliability and practical robustness. Existing benchmarks mostly target high-resource languages and provide limited coverage for low-resource settings, such as Turkish. Moreover, existing studies on Turkish document parsing lack a standardized benchmark that reflects real-world scenarios and document diversity. To address this gap, we introduce OCRTurk, a Turkish document parsing benchmark covering multiple layout elements and document categories at three difficulty levels. OCRTurk consists of 180 Turkish documents drawn from academic articles, theses, slide decks, and non-academic articles. We evaluate seven OCR models on OCRTurk using element-wise metrics. Across difficulty levels, PaddleOCR achieves the strongest overall results, leading most element-wise metrics except figures and attaining high Normalized Edit Distance scores in easy, medium, and hard subsets. We also observe performance variation by document type. Models perform well on non-academic documents, while slideshows become the most challenging.