This book explores the automated translation of written Punjabi text into Indian Sign Language (ISL) using advanced technology. Millions of deaf individuals face daily communication hurdles because text-based systems do not match visual sign languages. To solve this, Natural Language Processing (NLP) bridges spoken text and visual gestures automatically. This book presents a complete framework to convert written Punjabi sentences into smooth, animated Indian Sign Language avatars.
Readers will first learn the unique structures of Indian Sign Language. Sign language is not just a direct copy of spoken words. It features its own distinct grammar, hand shapes, movement paths, and facial expressions. The book then explains how modern machine learning models make translation possible. You will discover how deep learning algorithms learn patterns from parallel datasets to map complex Punjabi phrases into the correct visual signs.
A large part of the book focuses on linguistic analysis. It breaks down the process of Part of Speech (POS) tagging to identify nouns, verbs, and adjectives in Punjabi text. Accurate tags help the system decide which visual signs to display. Next, the text covers syntactic parsing, which breaks down sentences into clear grammatical structures. This step matches spoken grammar rules with the spatial grammar of ISL, fixing confusing phrasing before it reaches the generation engine.
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