GAIDA: Generative AI for Human-in-the-Loop Document Annotation
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 GAIDA, a research prototype serving as its reference implementation.
ProofPaper ↗
Key points
- 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.
- 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 80.9%, and reviewing LLM pre-annotations reduced semantic-labeling time by up to 73.7%, despite imperfect pre-annotations of 78.86% and 86.98% Micro F1 on FUNSD and WildReceipt, respectively.
- Finally, we reuse the same implementation, adapting only OCR and prompt configuration, to build and openly release AraReceipt, a manually annotated dataset of 100 Arabic receipt images labeled with 25 key-information classes.
Sources (1)
- [1]GAIDA: Generative AI for Human-in-the-Loop Document AnnotationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:22 PM
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 GAIDA, a research prototype serving as its reference implementation.
Document annotation is a fundamental yet labor-intensive step in building document information extraction systems.
Extractive summary: sentences quoted from the sources.
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