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- W4387341918 abstract "Significant challenges arise for individuals who are deaf and mute, as effective communication is crucial in today's world. Bridging the communication gap is of utmost importance, and advancements in machine learning offer a solution. This research focuses on developing a Hindi sign language detection system to address the needs of Hindi speakers in India, where Hindi is the most spoken language. By enabling communication in Hindi through sign language, this system ensures that individuals who are not proficient in English can fully participate in society and access education effortlessly. Existing systems primarily cater to American Sign Language (ASL) and Indian Sign Language (ISL), leaving a gap for Hindi sign language. Through the proposed methodology and leveraging machine learning techniques, the system aims to revolutionize the lives of the deaf and mute, empowering them to express themselves, interact seamlessly, and be understood by a wider audience. By addressing this crucial problem, the research contributes to inclusivity, accessibility, and equal opportunities for Hindi speakers." @default.
- W4387341918 created "2023-10-05" @default.
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- W4387341918 date "2023-10-02" @default.
- W4387341918 modified "2023-10-06" @default.
- W4387341918 title "Hindi Sign Language Detection using CNN" @default.
- W4387341918 doi "https://doi.org/10.36948/ijfmr.2023.v05i05.6962" @default.
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