Deep Neural Framework for Automated Kannada Character Recognition Using Threshold-Driven Feature Analysis

Abstract

The recognition of handwritten Kannada characters remains a challenging research problem due to the script’s complex structural variations, diverse stroke patterns, and high intra-class variability. Conventional character recognition approaches often struggle to achieve reliable performance when confronted with irregular handwriting styles, noisy inputs, and variations in character morphology. This research presents a Deep Neural Framework for Automated Kannada Character Recognition Using Threshold-Driven Feature Analysis, designed to improve script interpretation through integrated preprocessing, adaptive threshold segmentation, feature optimization, and deep neural representation learning. The proposed framework establishes a systematic recognition pipeline where threshold-driven segmentation enhances character boundary identification, while deep neural modeling captures hierarchical spatial and structural features. Existing research on Kannada handwritten recognition has demonstrated the effectiveness of spatial feature extraction, wavelet-based representations, divide-and-conquer strategies, and neural classifiers; however, limitations remain regarding robustness, scalability, and generalized feature learning. The proposed approach addresses these limitations by combining adaptive feature analysis with deep learning principles to create a more efficient recognition architecture. The study theoretically positions deep neural feature learning as a solution for overcoming traditional handcrafted feature constraints and provides insights into automated digitization of Kannada documents, archival systems, and multilingual intelligent applications. The findings indicate that a threshold-driven deep framework can enhance character representation, reduce segmentation errors, and support reliable handwritten script recognition.