ILAPP: An Efficient Design of Sign Language Recognition System using Intelligent Learning Assisted Prediction Principles

In this study, we introduce ILAPP (Intelligent Learning Assisted Prediction Principles), an innovative design for a Sign Language Recognition System. ILAPP harnesses the power of Convolutional Neural Networks (CNN) for efficient feature extraction and employs the renowned LeNet-5 Model for robust classification. Sign language recognition is a critical technology that bridges communication gaps for individuals with hearing impairments. Our system, ILAPP, offers an efficient and effective solution to this challenge. The core of ILAPP lies in its utilization of CNNs to extract essential features from sign language images, capturing intricate patterns and nuances crucial for accurate classification. The LeNet-5 Model is employed for classification, benefiting from its proven capabilities in image recognition tasks. Our research focuses on enhancing the accuracy and efficiency of sign language recognition, aiming to provide a practical tool for the deaf and hard-of-hearing community. ILAPP represents a significant advancement in this field, and its promising results underscore its potential for real-world applications. This study showcases the fusion of intelligent learning principles with state-of-the-art models, highlighting the importance of leveraging AI in addressing accessibility and communication challenges.In our study, the proposed model demonstrates an impressive accuracy rate of 98.23% when compared to existing models. This high accuracy rate signifies the exceptional performance and effectiveness of our model in sign language recognition.