Braille refers to a blind or visually impaired person's writing technique used to read and write. Raised dots are read through the use of fingertips by the readers. Technological advancements are a challenge regarding accessibility since people who can see cannot easily read or understand Braille information. Therefore, measures must be taken to promote accessibility and enable Braille content to be read and heard with ease through digital channels. Enabling digital input of Braille material can enhance the interaction between visually impaired and sighted people, demanding further study for successful methods. In the present scenario, several methods like Optical Character Recognition (OCR), Machine Learning (ML), and Deep Learning (DL) are employed for Braille character recognition. However, these methods have some challenges, like reduced accuracy, processing limitations for skewed images, and fewer real-time applications. To overcome the above limitations, the current research presents a new deep learning approach to ensure Braille character recognition with high accuracy. The system obviates the requirement of manual transcription and thus enhances efficiency, speed, and usability while processing Braille text. Additionally, the system contains a Text-to-Speech (TTS) module that transcribes the recognized text-to-speech, hence making it more accessible to individuals with varying needs. Developed for versatility, it accommodates multiple forms of Braille, whether embossed or printed, for compatibility across different media. By effortlessly translating Braille into text and voice, this technology bridges the communication gap between visually impaired and sighted people, enabling greater accessibility in education, communication, and information exchange. Overall, this solution makes society more inclusive, with Braille material becoming easier to understand, utilize, and widely accessible.