Enhancing Asian Indigenous Language Processing through Deep Learning-Based Handwriting Recognition and Optimization Techniques

Abstract

Asian indigenous language or autochthonous language is a  language  which is native to a region and spoken by  indigenous people in Asia. This language is a linguistically different  community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages are attained important internet amongst research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is most ancient Indian language that is mostly exploited in Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs capsule network (CapsNet) model for feature vector generation with HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.