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20232026
most citedDocling Technical Report

12 citations · 21 across the 12 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CV2026

DeskForge: Dense Supervision from Desktop Environments for Computer-Use Agents

A. Said Gurbuz, Ahmed Nassar, Sunghwan Hong +2

Computer-use agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually similar controls compete for at…

cs.CV2026

Identify, Locate, Link: End-to-End Key-Value Extraction from Document Images

A. Said Gurbuz, Ahmed Nassar, Christoph Auer +8

Document processing pipelines traditionally cascade optical character recognition (OCR) engines with downstream models for structured information extraction, leading to multi-stage…

cs.CL2026

DocAtlas: Multilingual Document Understanding Across 80+ Languages

Ahmed Heakl, Youssef Mohamed, Abdullah Sohail +6

Multilingual document understanding remains limited for low-resource languages due to scarce training data and model-based annotation pipelines that perpetuate existing biases. We…

cs.CV2026

Structured Layout Priors for Robust Out-of-Distribution Visual Document Understanding

Peter El Hachem, Ahmed Nassar, A. Said Gurbuz +2

Vision-Language Models (VLMs) parse documents end-to-end but frequently break down on layouts unlike those seen in training. We attribute this to a two-hop bottleneck: before the d…

cs.CV2026★ 1 cited

MarkushGrapher-2: End-to-end Multimodal Recognition of Chemical Structures

Tim Strohmeyer, Lucas Morin, Gerhard Ingmar Meijer +3

Automatically extracting chemical structures from documents is essential for the large-scale analysis of the literature in chemistry. Automatic pipelines have been developed to rec…

cs.CV2026

ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision

A. Said Gurbuz, Sunghwan Hong, Ahmed Nassar +2

Modern computer-use agents (CUA) must perceive a screen as a structured state, what elements are visible, where they are, and what text they contain, before they can reliably groun…