Sign Language into Text Messages Using Convolution Neural Network

Sign language is an ancient and most common form of communication. Not many people understand it, so sign interpreters also find it hard to understand. The advancement of neural networks allows for more efficient sign language recognition systems, especially in Sign Language fingerspelling. Different research is there to detail the effective recognition of gestures using convolutional neural networks and deep learning techniques. The high accuracy achieved by a few of these models can exceed the 99.4% classification rate on training and test datasets, and they have performed better in recognising cases than humans. This approach demonstrated promising results for finger-writing-based signs by fingerprinting the neural network-based hand gesture recognition and classification. However, we can preprocess hand gestures with filters and use other classification algorithms to improve the accuracy of our model. Future developments in smart gloves and sign language recognition will lead to even better modes of communication for the deaf community, reducing barriers between users of signing languages and non-signing individuals. Subsequent research and data gathering are essential for improving the generalisation and effectiveness of sign language recognition systems.