32 citations · 89 across the 6 of their papers we have counts for
7 papers
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…
Vehicle classification based on convolutional networks applied to FM-CW radar signals
Samuele Capobianco, Luca Facheris, Fabrizio Cuccoli +1
This paper investigates the processing of Frequency Modulated-Continuos Wave (FM-CW) radar signals for vehicle classification. In the last years deep learning has gained interest i…
DocEmul: a Toolkit to Generate Structured Historical Documents
Samuele Capobianco, Simone Marinai
We propose a toolkit to generate structured synthetic documents emulating the actual document production process. Synthetic documents can be used to train systems to perform docume…
Record Counting in Historical Handwritten Documents with Convolutional Neural Networks
Samuele Capobianco, Simone Marinai
In this paper, we investigate the use of Convolutional Neural Networks for counting the number of records in historical handwritten documents. With this work we demonstrate that tr…