DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
This paper proposes DocOCR-Eval, an annotation-free evaluation framework for automatic OCR assessment and selection. It employs a three-staged correction and ranking strategy to approximate annotation-based tool ordering without ground-truth labels. Experiments show that aggregating multiple MLLMs improves alignment with human rankings, enabling reliable tool selection in label-limited settings.
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[Submitted on 5 May 2026]
Title:DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
View a PDF of the paper titled DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth, by Zihan Xu and 5 other authors
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Abstract:Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and layout information. While numerous Optical Character Recognition (OCR) engines and multimodal large language models (MLLMs) have been developed for this purpose, selecting an appropriate document parsing solution for a given document collection remains challenging, particularly in label-scarce settings. In this work, we conduct a systematic evaluation of text recognition performance across a diverse set of OCR engines and state-of-the-art MLLMs on multiple scanned document benchmarks spanning different domains and languages. Motivated by the limited contextual reasoning capabilities of many OCR engines and the high cost of manual annotations, we propose DocOCR-Eval, an annotation-free evaluation framework for automatic OCR assessment and selection. DocOCR-Eval employs a three-staged correction and ranking strategy to approximate annotation-based tool ordering without ground-truth labels. We show that aggregating across multiple MLLMs progressively improves alignment with annotation-based rankings. Extensive experiments further demonstrate that reliable OCR tool selection can be achieved in realistic, label-limited settings, providing practical guidance for deploying document parsing systems across diverse real-world document collections.
Comments: Work in progress
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2607.16203 [cs.LG]
(or arXiv:2607.16203v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.16203
arXiv-issued DOI via DataCite
Submission history
From: Yihao Ding [view email] [v1] Tue, 5 May 2026 10:59:34 UTC (927 KB)
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