HarmonyHands

dc.contributor.advisor	Adamkó, Attila Tamás
dc.contributor.author	Alsherif, Mohamed Alsayed Mohamed Alsayed
dc.contributor.department	DE--Informatikai Kar
dc.date.accessioned	2025-02-22T20:18:43Z
dc.date.available	2025-02-22T20:18:43Z
dc.date.created	2024
dc.description.abstract	Harmony Hands" is a project designed to bridge communication gaps between hearing individuals and the deaf or hard-of-hearing community by translating sign language gestures into spoken or written text. The system uses a convolutional neural network (CNN) developed with TensorFlow for recognizing gestures in real time, utilizing computer vision techniques. The frontend is built with React for a responsive user interface, while the backend employs Django, facilitating data management through a RESTful API. The project also utilizes MariaDB for database management, ensuring efficient data storage. Although it currently focuses on one sign language, the system is scalable for future expansions to other languages and dialects, aiming to make communication more accessible and inclusive across various settings.
dc.description.course	Mérnökinformatikus
dc.description.degree	BSc/BA
dc.format.extent	44
dc.identifier.uri	https://hdl.handle.net/2437/387317
dc.language.iso	en
dc.rights.access	Hozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subject	AI
dc.subject	Backend
dc.subject	Frontend
dc.subject.dspace	Informatics
dc.title	HarmonyHands

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