Sign Language Recognition Using CNN

A hearing-impaired person is never simple to con- verse with. The study presented in this paper is a reflection of the efforts undertaken to investigate the difficulties with character classification in Sign Language. When communicating with someone who cannot speak or who have hearing loss, sign language is insufficient. The motions made by individuals with impairments may look chaotic or disorganised to someone who has never learnt this language. It is advisable to employ both forms of communication. The identification of Sign Language based on CNN is shown in this paper Convolutional neural networks are used to train and then recognise our model, which is utilised to identify the pictures. The accuracy of our model is 92% .