Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Sign language recognition can be considered as an effective solution for disabled people to communicate with others. It helps them in conveying the intended information using sign languages without any challenges. Recent advancements computer vision and image processing techniques leveraged detect classify signs used by manner. Metaheuristic optimization algorithms designed a manner such that it fine tunes hyper parameters, Deep Learning (DL) models latter considerably impacts classification results. With this motivation, current study designs Optimal Transfer Driven Language Recognition Classification (ODTL-SLRC) model people. The aim of proposed ODTL-SLRC technique is recognize derives EfficientNet generate collection useful feature vectors. In addition, parameters involved are fine-tuned help HGSO algorithm. Moreover, Bidirectional Long Short Term Memory (BiLSTM) employed classification. was experimentally validated benchmark dataset results were inspected under several measures. comparative analysis established superior performance over recent approaches terms efficiency.