Nearest quad-tree (NQT) classifier: a novel framework for handwritten character recognition

Handwritten character recognition (HCR) is a critical component of diverse applications, especially in multilingual regions such as India. Despite technological advancements, recognizing handwritten characters remains challenging because of variations in writing style, shape and linguistic nuances. This study presents an efficient HCR system tailored for Indian languages, such as Tamil and Telugu scripts, to address these challenges. The proposed nearest quad-tree (NQT) classifier enhances recognition accuracy while ensuring computational efficiency. This novel approach, a light-weight design, combines local feature-level decisions with a global decision-making process, thereby making it suitable for devices with limited computational resources. Key contributions include the emphasis of the NQT classifier on efficiency and lightweight design, applicability to regional languages and usability in resource-constrained settings. This study utilized three datasets from the Tamil, Devanagari and Telugu languages to demonstrate the exceptional performance of the NQT classifier in character recognition through the minimization of misclassification errors. The classifier exhibited consistently low misclassification error rates across a diverse range of characters, outperforming existing deep learning models. Notably, the NQT classifier achieved an overall accuracy, precision, recall and F1-measure of 96%, 95%, 97% and 96%, respectively, across the three datasets, surpassing the performance of popular transfer learning models. Furthermore, a comprehensive computational efficiency analysis highlighted the superior efficiency of the NQT classifier in terms of training time, CPU and GPU utilization and memory requirements, underscoring its potential for practical applications in resource-constrained environments.