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- W4320801758 abstract "A very important category of computational intelligence with respect to social applicability is the ability to transform any linguistic sign gesture into corresponding language text. This is formally known as Sign Language translation (SLT). Quite recently, SLT has been employed by making use of Convolution Neural Networks Models (CNN models) and are decently successful in achieving the target goal. SLT focuses on population who are hearing or speech-impaired. The technique, in focus, takes users' hand gestures as input images or videos and captures the relevant features to convert the same into an appropriate linguistic symbol. The entire process makes it convenient for users to interact with the world and vice versa. In an attempt to make life easier for a certain focused section of the society, SLT is widely studied and a large section of research is invested in this area. SLT gives out the results in the form of physical carriages, signal sets and/or countenances to deliver a particular message across. It is also helpful in delivering the correct emotional state of differently-abled people so that normal people can understand them better. This paper proposes a machine learning model which successfully and efficiently classifies hand gestures into spellings and sentences. Our survey study includes object detection and classification stages. In the preliminary stage, 10 english language letters are repeatedly fed into the system using varied signal images owing to background changes, lighting effects and positions variations. Training and testing data spaces are kept different to yield better result confidence. CNN algorithms are applied aggressively to improve the conversion accuracy." @default.
- W4320801758 created "2023-02-15" @default.
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- W4320801758 date "2022-11-09" @default.
- W4320801758 modified "2023-09-30" @default.
- W4320801758 title "A Vision-based Smart Human Computer Interaction System for Hand-gestures Recognition" @default.
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- W4320801758 doi "https://doi.org/10.1109/iccst55948.2022.10040464" @default.
- W4320801758 hasPublicationYear "2022" @default.
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