Giuseppe De Gregorio

Dr. Giuseppe De Gregorio

Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Italy
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Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 March 2022 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Closed access
NCNicole D. Cilia
Nicole D. Cilia
Giuseppe De GregorioGiuseppe De Gregorio
Giuseppe De Gregorio
CSClaudio De Stefano
Claudio De Stefano
Francesco FontanellaFrancesco Fontanella
Francesco Fontanella
Angelo MarcelliAngelo Marcelli
Angelo Marcelli
Antonio ParzialeAntonio Parziale
Antonio Parziale

21 February 2021
Verified
Book Chapter
Closed access
PDF file
Giuseppe De GregorioGiuseppe De Gregorio
Giuseppe De Gregorio
DDDomenico Desiato
Domenico Desiato
AMAngelo Marcelli
Angelo Marcelli
GPGiuseppe Polese
Giuseppe Polese


25 November 2022
Verified
Book Chapter
Closed access
PDF file
Giuseppe De GregorioGiuseppe De Gregorio
Giuseppe De Gregorio
SBSanket Biswas
Sanket Biswas
MSMohamed Ali Souibgui
Mohamed Ali Souibgui
ABAsma Bensalah
Asma Bensalah
JLJosep Lladós
Josep Lladós
Angelo MarcelliAngelo Marcelli
Angelo Marcelli


3 November 2020 in Journal of Imaging
Verified
Journal Article
Open access
PDF file
Angelo MarcelliAngelo Marcelli
Angelo Marcelli
Giuseppe De GregorioGiuseppe De Gregorio
Giuseppe De Gregorio
Adolfo SantoroAdolfo Santoro
Adolfo Santoro
This paper proposes a performance model for estimating the user time needed to transcribe small collections of handwritten documents using a keyword spotting system (KWS) that provides a number of possible transcriptions for each word image. The model assumes that only information obtained from a small training set is available, and establishes the constraints on the performance measures to achieve a reduction of the time for transcribing the content with respect to the time required by human experts. The model is complemented with a procedure for computing the parameters of the model and eventually estimating the improvement of the time to achieve a complete and error-free transcription of the documents.