Sign Language Detection Using Convolutional Neural Networks (CNN)

Convolutional Neural network (CNN) was used to analyze sign language detection and create a workable system that might be used in the future. Neural networks have had a significant impact on the development of sign language identification, as well as the role that machine learning plays in this process, as well as the feasibility and efficiency of a python program in this area. Various ideas and previous literature were examined regarding the study’s focus on computational learning. As part of this inquiry, data was acquired from primary and secondary sources. Qualitative and experimental research aims were combined in the development of a convolutional neural network (CNN) prototype for the detection of sign language utilizing a critical literature review. Given that CNN has shown outstanding results in image categorization and pattern recognition tasks, this study presents a deep learning technique for developing a reliable and real-time ASL identification system using CNN with an accuracy of 99%.