Sign Language Recognition and Translation into Pashto Language Alphabets

Sign language is mandatory and mostly used by huge number of individuals (particularly disabled) in society and it is another way of communication; The form of the hand, the contour of the movement, and the posture of the hand, face, and body parts are all distinct in each sign language; as a result, visual sign language identification is a challenging area of computer vision research; various academics have suggested several models in recent years that have been greatly improved using deep learning approaches. This study looked at the vision-based sign language recognition models suggested utilizing deep learning approaches in the last five years. Although the suggested model’s general trend reveals that sign language recognition accuracy has substantially increased, specific problems still have to be overcome. There are various and multiple languages in the world, people from different languages, have their alphabets and sign to communicate with each other. Many language speakers have developed a way to communicate with disabled people like Indian sign Language, French sign language several more; In this research paper, a novel Pashto sign language has been worked on. The most spoken language in southern Afghanistan is the Pashto language, and it has its grammar and history; it has approximately 40 million population in the world. The aim is to make a system by which disabled people of this language can communicate with the society. The system will take the signs and gestures of disabled people to change them into the Alphabet of Pashto language. 2500 dataset images are prepared and CNN model i s a ppl i e d to recognize the hand gestures and have executed the model with an accuracy of 98%.