DC Field | Value | Language |
dc.contributor.author | AHRES, FAtima | - |
dc.contributor.author | ZAITI, RAchida | - |
dc.date.accessioned | 2022-06-06T08:28:14Z | - |
dc.date.available | 2022-06-06T08:28:14Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/298 | - |
dc.description | Mr Bensenane H. Encadreur Mr Rahmoun A. Co-Encadreur | en_US |
dc.description.abstract | Gesture recognition is becoming a growing field of study with too many applications such as
automated sign language recognition, human-machine interaction and medical application.
In this project, we try to build a hand gesture recognition system that translates Algerian sign
language to speech. We present the different technical approaches and algorithms used to
detect and recognize the hand gesture and the hand gesture recognition stages from hand
detection to recognition, we analyze the existing sign language application and compare them
to highlight their weaknesses and strength factors.
Moreover, we extract the functionality considerations and product construction considerations
and based on them and the better approach we chosen we build our system and deploy it in a
web application.
In addition we present the software tools and AI technologies used to build our recognition
models, and to develop our web application and we present the final result. | en_US |
dc.language.iso | en | en_US |
dc.title | Hand gesture recognition | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Ingénieur
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