activity
20242026
collaborators

7 papers

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.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.CV2025

Advanced Layout Analysis Models for Docling

Nikolaos Livathinos, Christoph Auer, Ahmed Nassar +16

This technical report documents the development of novel Layout Analysis models integrated into the Docling document-conversion pipeline. We trained several state-of-the-art object…

cs.CV2025

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

Ahmed Nassar, Andres Marafioti, Matteo Omenetti +10

We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a…

cs.CV2025

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence

Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60

We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…

cs.CL2025

Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion

Nikolaos Livathinos, Christoph Auer, Maksym Lysak +14

We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a uni…