32 citations · 55 across the 6 of their papers we have counts for
6 papers
Towards Reliable and Interpretable Document Question Answering via VLMs
Alessio Chen, Simone Giovannini, Andrea Gemelli +2
Vision-Language Models (VLMs) have shown strong capabilities in document understanding, particularly in identifying and extracting textual information from complex documents. Despi…
BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations
Simone Giovannini, Fabio Coppini, Andrea Gemelli +1
We present a unified dataset for document Question-Answering (QA), which is obtained combining several public datasets related to Document AI and visually rich document understandi…
CTE: A Dataset for Contextualized Table Extraction
Andrea Gemelli, Emanuele Vivoli, Simone Marinai
Relevant information in documents is often summarized in tables, helping the reader to identify useful facts. Most benchmark datasets support either document layout analysis or tab…
Data augmentation on graphs for table type classification
Davide del Bimbo, Andrea Gemelli, Simone Marinai
Tables are widely used in documents because of their compact and structured representation of information. In particular, in scientific papers, tables can sum up novel discoveries…
Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents
Andrea Gemelli, Emanuele Vivoli, Simone Marinai
Tables are widely used in several types of documents since they can bring important information in a structured way. In scientific papers, tables can sum up novel discoveries and s…
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
Andrea Gemelli, Sanket Biswas, Enrico Civitelli +2
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Network…