Our Aim is to create a system that can provide an accessible way for the deaf and blind to access audio-video content, by providing a Speech to Braille translator and Sign Language Recognition System. This system should be able to convert speech to braille, detect signs in order to teach sign language, and output letters corresponding to the signs shown. This system will help bridge the gap between the differently-abled and modern society, by providing a two-way approach, allowing normally-abled people to learn braille script and sign language as well.The Speech to Braille project has a two step process. The 1st step is to convert speech to text using a speech recognition library like SpeechRecognition. The 2nd step is to convert the text to braille. In order to convert speech to text, the speech recognition library will listen to the user’s voice and convert it to a text string or will take input audio file and convert it into a text string. The text string can then be parsed using a natural language processing library to extract meaningful information from the text string. The second step is to convert the text string to braille. The text obtained is then converted to Braille using a predefined mapping of English alphabets and numbers to their corresponding Braille characters.The braille string can then be displayed on a braille display grid.Sign language detection is a process of recognizing the sign language gestures and converting them into text. This process is achieved by using CV, ML and Neural Networks. First, the image of the sign language gesture is captured using a camera. Then, the image is processed in order to extract the features of the sign language gesture. This is done by applying filters, such as Gaussian blur, adaptive thresholding. Once the features of the sign language gesture are extracted, they are fed into a machine learning model.. Once the prediction is made, the sign language gesture is converted into text and this used to make sentences