GAIDA: Generative AI for Human-in-the-Loop Document Annotation

In an initial single-annotator, stage-specific study on the FUNSD scanned-form and WildReceipt retail-receipt benchmarks, OCR assistance reduced region-creation and transcription time by up to \textbf{80.9\%}, and reviewing LLM pre-annotations reduced semantic-labeling time by up to \textbf{73.7\%}, despite imperfect pre-annotations of 78.86\% and 86.98\% Micro F1 on FUNSD and WildReceipt, respectively. It comes from a paper posted to arXiv on 20 October 2026. Document annotation is a fundamental yet labor-intensive step in building document information extraction systems. While Optical Character Recognition (OCR) and Large Language Models (LLMs) can automate parts of this process, we argue that annotation systems should be judged not by how completely they remove human effort, but by how effectively they redirect it toward review and correction. We propose a modular human-in-the-loop architecture that integrates OCR-based extraction, LLM-based semantic pre-annotation, and human validation in a single editable workflow, and present \textbf{GAIDA}, a research prototype serving as its reference implementation. GAIDA supports interchangeable OCR engines, LLM backends, and prompt configurations, and enables stage-specific measurement of annotation effort. Finally, we reuse the same implementation, adapting only OCR and prompt configuration, to build and openly release \textbf{AraReceipt}, a manually annotated dataset of 100 Arabic receipt images labeled with 25 key-information classes. The results indicate that imperfect automated outputs provide practical value when presented as editable suggestions under human control.

Why it counts

In an initial single-annotator, stage-specific study on the FUNSD scanned-form and WildReceipt retail-receipt benchmarks, OCR assistance reduced region-creation and transcription time by up to \textbf{80.9\%}, and reviewing LLM pre-annotations reduced semantic-labeling time by up to \textbf{73.7\%}, despite imperfect pre-annotations of 78.86\% and 86.98\% Micro F1 on FUNSD and WildReceipt, respectively.

Sources

Primary source: primary source

What is not known

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