International Journal For Multidisciplinary Research

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Hindi Sign Language Detection using CNN

Author(s) Ifham Khwaja, Kaushik Rathod, Naman Sanklecha, Pragya Sinha
Country India
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.
Keywords CNN, OpenCV, pooling layer, VGG, Epochs
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-10-02
Cite This Hindi Sign Language Detection using CNN - Ifham Khwaja, Kaushik Rathod, Naman Sanklecha, Pragya Sinha - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.6962
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.6962
Short DOI https://doi.org/gstc9j
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