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Sign language is an expressive visual language that uses the hand and face and is an important tool for deaf individuals to communicate. Standard means of recognition objectives cannot recognize complex gestures effectively, in turn limiting accessibility and transactions of communication effectively. This project seeks to close communication barriers by translating hand gestures into speech, text, and even serve as an output to virtual assistants. To date, prior research in signing recognition has focused primarily on identifiable hand gestures, often selecting a subset of Indian Sign Language (ISL) for classification. In this work, a deep learning method is proposed to perform static sign recognition through Convolutional Neural Networks (CNN) with the VGG19 architecture. The system is trained through a dataset of ISL gestures for predicting and classifying signs with high confidence. The predicted sign is converted to a text output, to then a speech output, stored as an audio file. In conclusion, the system provides accessibility through detection, enabling a user user experience for both signers/non-signers to communicate and add encryption with accessibility. The model produces a far more particular/accurate performance in sign language recognition, and follows through with promising assistive technology solutions.