Hierarchical Natural Language Processing Architecture for Multilingual Script Identification and N-Gram Fingerprinting

This paper presents a hierarchical Natural Language Processing architecture for multilingual language identification. The proposed system combines Unicode script analysis, character bigram and trigram fingerprinting, and multilingual stopword matching to distinguish languages across multiple writing systems. Implemented using client-side JavaScript, the system performs language identification within the browser and provides an interactive visualizer for examining intermediate linguistic features and candidate rankings. A reported Spanish-language benchmark ranks Spanish above several closely related Latin-script languages. The architecture emphasizes interpretability, lightweight execution, and privacy by avoiding external inference APIs. Further evaluation on larger multilingual datasets is needed to establish general classification performance.