Sign language is a way of transfer feelings and our thoughts non-verbally. People who are hearing impaired, dumb or speechless use sign language as their primary means of communication. For communication these people apply gestures which are based on hand signals to share their ideas. Sadly, the overwhelming most of the individuals aren’t awake to the linguistics of those gestures. In a try to overcome these gaps, we offer real time Sign Language identification system which is based on the American Sign Language (ASL) Dataset. The system we proposed uses the CNN (Convolutional Neural Network) algorithm to recognize and interpret static hand signals of letters relating to American Sign Language into written output. An Android based application is produced for this system and further it can convert the text into Speech. The text on the screen is translated into voice using a text-to-speech feature. Text-to-speech is frequently employed as an accessibility tool to aid individuals who struggle to read text on screens, but it is also practical for
those who want to be read to. This feature has proven to be quite popular and helpful for users.
Keywords
American Sign Language Convolutional Neural Network, Deep learning, gesture recognition, hand gesture to speech.
Conclusion
The project is a straightforward illustration of how CNN may be used to tackle computer vision problems very accurately. It is possible to translate sign language using finger spelling for speech impaired people. The initiative can address a portion of the Sign Language translation challenge because sign languages are spoken more in context than as finger spelling languages. The primary goal has been accomplished, namely, the requirement for an interpreter has been removed. We illustrated the learning and overall performance of the model. Using basic tools and simplified techniques the strategy is far beneficial for Speech Impaired Person. The created model will make a contribution immensely to the people who want to express their thoughts with the people who don’t understand the sign language. By using this android application, user can join the alphabets to make the sentences and with the help of Text to Speech it will read out loud for the other people.
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How to Cite This Paper
Nannuri Abhilash, Lingala Veera Brahmachari, Lanka Kiran Babu, Mrs.C. Merylne Sandra Christina (2026). Real-Time Sign Language Translator. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
