Numerous studies are being undertaken to provide answers forsign language recognition and classification. Deep learning-basedmodels have higher accuracy (90%-98%); however, require moreruntime memory and processing in terms of both computationalpower and execution time (1 hour 20 minutes) for feature extrac-tion and training images. Besides, deep learning models are notentirely insensitive to translation, rotation, and scaling; unless thetraining data includes rotated, translated, or scaled signs. However,Orientation-Based Hashcode (OBH) completes gesture recognitionin a significantly shorter length of time (5 minutes) and with rea-sonable accuracy (80%-85%). In addition, OBH is not affected bytranslation, rotation, scaling, or occlusion. As a result, a new inter-mediary model is developed to detect sign language and performclassification with a reasonable processing time (6 minutes) likeOBH while providing attractive accuracy (90%-96%) and invariancequalities. This paper presents a coupled and completely networked autonomous system comprised of OBH and Gabor features with machine learning models. The proposed model is evaluated with 576 sign alphabet images (RGB and Depth) from 24 distinct categories, and the results are compared to those obtained using traditional machine learning methodologies. The proposed methodology is 95.8% accurate against a randomly selected test dataset and 93.85% accurate after 9-fold validation.