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- W4386114967 abstract "Sign language is one of the oldest and most natural forms of language for communication. Sign language is used by speech-impaired persons for communication. For a regular individual, sign language interpretation is exceedingly challenging. This project effort proposes a real-time method for finger spelling-based American sign language using neural networks. The hand is first put through a Gaussian filter in this manner to get rid of the noise. A classifier is used to predict the class of the hand gestures from the filter’s output. It displays the textformatted output for a specific sign from that class. Python’s pyttsx3 module is used to convert text to speech. Key Words: American sign language, neural networks,Gaussian filter,pyttsx3" @default.
- W4386114967 created "2023-08-24" @default.
- W4386114967 creator A5092685508 @default.
- W4386114967 date "2023-08-22" @default.
- W4386114967 modified "2023-10-14" @default.
- W4386114967 title "Sign Language Recognition and Conversion to Audio using Image Processing and Machine Learning" @default.
- W4386114967 doi "https://doi.org/10.55041/ijsrem25367" @default.
- W4386114967 hasPublicationYear "2023" @default.
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