VLM-based harmonization of inconsistent annotations across two document layout corpora raises detection F-score from 0.860 to 0.883 and table TEDS from 0.750 to 0.814 while tightening embedding clusters.
LayoutLM: Pre-training of text and layout for document image understanding
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W-RAC decouples extraction from semantic planning via structured units and LLM grouping to match traditional retrieval performance at roughly 10x lower LLM token cost.
A multi-stage LLM pipeline for structure-preserving Marathi-to-English translation of government PDFs using layout-aware OCR and HTML reconstruction.
MADP multi-agent pipeline with human-in-the-loop achieves 97% full automation on 955 real documents, 98.5% accuracy on ablation set, and 69-70% reductions in FTE, energy, and emissions versus manual processing.
Frontier multimodal LLMs achieve ~85% accuracy and ~90% weighted F1 on digitizing complex handwritten medical forms, with Gemini 3.1 strongest overall and prompt optimization lifting macro metrics over 60%.
Few-shot prompting lifts F1 scores above 96 percent on electricity-invoice extraction for Gemini 1.5 Pro and Mistral-small, while hyperparameter changes produce only marginal gains.
citing papers explorer
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Improving Layout Representation Learning Across Inconsistently Annotated Datasets via Agentic Harmonization
VLM-based harmonization of inconsistent annotations across two document layout corpora raises detection F-score from 0.860 to 0.883 and table TEDS from 0.750 to 0.814 while tightening embedding clusters.
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From Handwriting to Structured Data: Benchmarking AI Digitisation of Handwritten Forms
Frontier multimodal LLMs achieve ~85% accuracy and ~90% weighted F1 on digitizing complex handwritten medical forms, with Gemini 3.1 strongest overall and prompt optimization lifting macro metrics over 60%.