The deaf community communicates primarily through the use of sign language. In general, sign language is much more figuratively formable for communication, which helps to advance and broaden the conversation. The ASL is regarded as the universal sign language, although there are numerous variations and other sign systems used in various parts of the world. There are fewer major ideas and concepts assigned. There are fewer principal ideas and assigned appearances in sign language. The main goal of this effort is to create a system of sign language that will benefit the deaf community and speed up the process of communication. The project's main objective is to build a classifier-based software model for sign language recognition. The strategy for this is to identify the gestures and use classifiers to assess the attributes. Principal component analysis is used for gesture recognition, and a classifier is used to assess the gesture features. The hand gesture has been used as a form of communication since the beginning of time. Recognition of hand gestures makes human-computer interaction (HCI) more versatile and convenient. Because of this, accurate character identification is crucial for a tranquil and error-free HCI. The majority of the hand gesture recognition (HGR) systems now in use have only taken a few straightforward discriminating motions into account for recognition performance, according to a literature review. This study uses robust modelling of static signs in the context of sign language recognition by using convolutional neural networks (CNNs) based on deep learning. In this study, CNN is used for HGR, which takes into account both the ASL alphabet and numbers simultaneously. The CNNs utilised for HGR are emphasized, along with their benefits and drawbacks. Modified Alex Net and modified VGG16 models for classification form the foundation of the CNN architecture. After feature extraction, a multiclass support vector machine (SVM) classifier is built, which is based on modified pre-trained VGG16 and Alex Net architectures. To achieve the highest recognition performance, the results are assessed using various layer features. Both the leave-one-subject-out and a random 70-30 method of cross-validation were used to test the accuracy of the HGR schemes. This work also emphasises how easily each character can be recognised and how similar their motions are to one another. To show how affordable this work is, the experiments are run on a basic CPU machine as opposed to cutting-edge GPU hardware. The proposed system outperformed several cutting-edge techniques with a recognition accuracy of 99.82%

Keywords : Sign language, ASL(American Sign Language) , Deaf Community , Gestures , Human Computer Interaction , Hand Gesture Recognition, CNN( Convolution Neuron Network), SVM(Support Vector Machine)