Abstract
Hand gestures are one of the nonverbal communication modalities used in sign language. It is most often used by deaf and/or dumb individuals who have hearing or speech impairments to communicate with other deaf and/or dumb people or with normal people. Many manufacturers across the world have created various sign language systems; however they are neither adaptable nor cost- effective for end users. To address this, a model has been developed that provides a system prototype that can detect sign language automatically, allowing deaf and dumb individuals to transfer the message more successfully with normal people. The static visuals were processed with a convolutional neural network and the feature extraction approach, and each sign was trained with ten samples. Image processing methods are used to determine the fingertip location of static pictures and convert them to text. The suggested technique can recognize the signer's pictures that are taken in real time during the testing phase. The results demonstrate that the suggested Sign Language Recognition System is capable of accurately recognizing pictures.
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