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		<Title>Sign Language Regonization using CNN Algorithm with Meachine Learning Techniques</Title>
		<Author>M Akilana and M F Akila Lourdes</Author>
		<Volume>06</Volume>
		<Issue>02</Issue>
		<Abstract> Deaf and hardofhearing people use sign language a visual language to communicate with one other and with people who do not know sign language However due to a lack of accessibility and communication hurdles there is an increasing demand for technologies to help sign language users and the hearing community communicate A system called sign language recognition with text and audio tries to fill this gap by automatically decoding sign language motions into spoken or written words The procedure entails a number of processes  including image preprocessing feature extraction gesture detection and translation into text or speech The intricacy and variety of sign languagemotions are one of the major obstacles to text and audiobased sign language recognition As a result creating precise and trustworthy identification systems involves both a vast and varied collection of sign language motions as well as powerful machine learning methods like Convolutional Neural Networks CNNs and Long ShortTerm Memory LSTM networks The ability to recognise sign language using text and audio has the potential to significantly increase accessibility and communication for the deaf and hardofhearing community allowing them to interact more freely with the hearing community and take part more completely in society Along with other industries it has uses in entertainment healthcare and education Technology has the ability to revolutionise interpersonal communication and close the gap between groups speaking various languages as it develops and gets better </Abstract>
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<copyright-statement>Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved</copyright-statement>
<copyright-year>2026</copyright-year>
</permissions>
		</www.wjpsonline.org>
		