Everybody involved in a conversation must put effort into the conversation to be successful. People with normal hearing and speech capabilities can easily communicate with one another. However, the majority of the population has always found it difficult to communicate with people who have differing abilities without the help of a translator. This is why using sign language becomes essential. Widespread usage of sign language helps overcome this barrier of communication and ensures that those with special needs are able to effectively communicate and share their feelings with the general public. These are typically employed by people with hearing and speech impairments who rely heavily on nonverbal modes of communication to communicate with one another and others. Sign Language Recognition involves recognizing and interpreting hand gestures and being able to understand their meaning. There are multiple sign language systems used in different parts of the globe. Objective: This research aims to examine the various advancements made to the Indian Sign Language system to facilitate communication between the general public and individuals with speech and hearing impairments. Methods: The system presented employs Natural Language Processing and emerging technologies like Computer Vision to detect hand signs using Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Recurrent Neural Networks (RNN), LSTM (Long Short-Term Memory). Once the hand poses are captured by the webcam, various steps are performed on it, they are classified and lastly translated into English words.