Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches

Abstract

An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people’s diverse handwriting styles. Therefore, this paper aims to develop Kannada handwritten script recognition framework by ensemble methods. Initially, the essential Kannada handwritten text images are collected. These collected images are used for pre-processing with the CLAHE and filtering techniques and followed by segmentation with the active contour approach. The segmented images are utilized using the Adaptive Local Tetra Pattern with freeman chain code histogram techniques. Here, the parameters in the LTrP are tuned with Hybrid Honey Badger Henry Gas Solubility Optimization. Finally, the handwritten recognition is performed by Hybrid Feature Extraction with Ensemble Deep Learning (HFE-EDH). Further, the recognition rate is elevated with the parameter optimization in deep learning approaches using the HGSO + HBA algorithm. Throughout the result analysis, the accuracy of the designed method attains 96%. Thus, the proposed method reveals better performance regarding various performance measures.