12 citations · 25 across the 8 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
Docling Technical Report
Christoph Auer, Maksym Lysak, Ahmed Nassar +16
This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialize…
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents
Oshri Naparstek, Roi Pony, Inbar Shapira +15
In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains. This effort has attra…
ESG Accountability Made Easy: DocQA at Your Service
Lokesh Mishra, Cesar Berrospi, Kasper Dinkla +11
We present Deep Search DocQA. This application enables information extraction from documents via a question-answering conversational assistant. The system integrates several techno…
ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents
Christoph Auer, Ahmed Nassar, Maksym Lysak +3
Transforming documents into machine-processable representations is a challenging task due to their complex structures and variability in formats. Recovering the layout structure an…
Optimized Table Tokenization for Table Structure Recognition
Maksym Lysak, Ahmed Nassar, Nikolaos Livathinos +2
Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized…
TableFormer: Table Structure Understanding with Transformers
Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak +1
Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they…
Robust PDF Document Conversion Using Recurrent Neural Networks
Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak +7
The number of published PDF documents has increased exponentially in recent decades. There is a growing need to make their rich content discoverable to information retrieval tools.…