Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Besma Rabhi"'
Autor:
Yahia Hamdi, Houcine Boubaker, Besma Rabhi, Abdulrahman M. Qahtani, Fahd S. Alharithi, Omar Almutiry, Habib Dhahri, Adel M. Alimi
Publikováno v:
Engineering Science and Technology, an International Journal, Vol 35, Iss , Pp 101215- (2022)
The beta-elliptic model (BEM) has proven a great success in several applications such as handwriting recognition and analysis, handwriting identification, the effect of age on the kinematics of hand movements, etc. With the emergence of deep learning
Externí odkaz:
https://doaj.org/article/b132996886034c1db5915e68444a8590
Publikováno v:
Memetic Computing. 13:459-475
Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than off
For several decades, the offline handwriting recognition problem has escaped a satisfactory solution. In the field of online recognition, researchers have had more successful performance, but the ability to extract dynamic information from static ima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b3cd634d9d89fa10bad4356f0f4b72b
https://doi.org/10.36227/techrxiv.21347262
https://doi.org/10.36227/techrxiv.21347262
Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than off
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a30e4da20b50969eb62968281edf0a15
https://doi.org/10.36227/techrxiv.13902650
https://doi.org/10.36227/techrxiv.13902650
Publikováno v:
Computers, Materials & Continua. 69:3259-3274
The exponential increase in new coronavirus disease 2019 (COVID-19) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pn
Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14ede5fca77a3b7445d67df053a39200
https://doi.org/10.36227/techrxiv.13903661.v3
https://doi.org/10.36227/techrxiv.13903661.v3
Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than off
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::722382a2a1e9fa14315dca1ad83814f0
https://doi.org/10.36227/techrxiv.13902650.v1
https://doi.org/10.36227/techrxiv.13902650.v1
Publikováno v:
Document Analysis and Recognition – ICDAR 2021 Workshops ISBN: 9783030861971
ICDAR Workshops (1)
ICDAR Workshops (1)
Stroke reconstruction from offline handwriting is an important research field. This article presents Online Signal Restoration (OSR) using Arabic Handwriting Dhad Dataset competition organized at ASAR 2021. The goal of this competition is to collect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ab228277bf30ee3df13d957cf7451ba
https://doi.org/10.1007/978-3-030-86198-8_26
https://doi.org/10.1007/978-3-030-86198-8_26
The online signal is rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down. Their offline counterpart consists of a set of pixels. Thus, the online handwriting recognition accuracy is generally better than the off
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0067d3b6be8e9262524ca1a94350a5d9
https://doi.org/10.36227/techrxiv.13072193
https://doi.org/10.36227/techrxiv.13072193
Publikováno v:
ICDAR
In this paper, we present an original framework for offline handwriting recognition. Our developed recognition system is based on Sequence to Sequence model employing the encoder decoder LSTM, for recovering temporal order from offline handwriting. H