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About the solution
The AI-powered solution developed by Priyanjali Gupta is a real-time American Sign Language (ASL) to English translation system. Using the Tensorflow object detection API and transfer learning from a pre-trained model called ssdmobilenet, the solution is able to accurately recognise and interpret ASL signs. The dataset for training the model was manually collected by running an Image Collection Python file that captures images from a webcam for specific ASL signs such as "Hello," "I Love You," "Thank you," "Please," "Yes," and "No."
The system demonstrates impressive capabilities in a demo video shared by Priyanjali Gupta on LinkedIn, showcasing its ability to instantly translate ASL signs into English text. The innovative idea behind this solution has garnered significant attention, with nearly 60,000 likes on the platform.
Priyanjali Gupta was inspired to develop this solution after a thought-provoking taunt from her mother. It sparked her contemplation on leveraging her knowledge and skills. Through conversations with virtual assistants like Alexa, the concept of inclusive technology resonated with her. Priyanjali acknowledges the complexity of creating a deep neural network for sign detection but remains dedicated to learning and further improving her solution.
She believes that normalising sign languages and other modes of communication for individuals with disabilities is crucial in bridging the communication gap. While many researchers and developers are working on similar technologies, Priyanjali Gupta's ASL to English translation system represents a significant step towards more seamless and effective communication for the hearing-impaired community.
More information available: https://analyticsdrift.com/indian-engineering-student-develops-ai-model-...
https://www.youtube.com/watch?v=E7X9CWn_ElQ
这些解决方案不应包括使用药物,化学品或生物制品(包括食品);创伤性设备;冒犯性的,商业或内在危险的内容。该解决方案未经医学验证。请谨慎进行!如果您有任何疑问,请咨询健康专家。
DISCLAIMER: This story was written by someone who is not the author of the solution, therefore please be advised that, although it was written with the utmost respect for the innovation and the innovator, there can be some incorrect statements. If you find any errors please contact the patient Innovation team via info@patient-innovation.com
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