Deepak Aggarwal

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About

Deepak Aggarwal is a researcher specializing in the intersection of natural language processing and deep learning, with a particular focus on signboard transliteration. His work addresses the critical challenge of converting multilingual text from visual signboards into standardized, machine-readable formats, a task essential for applications in navigation, tourism, and accessibility. Aggarwal’s most notable contribution, the 2021 paper "Review of Signboard Transliteration Using Deep Learning," provides a comprehensive survey of techniques that leverage neural networks to handle diverse scripts and noisy real-world images. While this work has garnered 2 citations, it serves as a foundational reference for researchers exploring automated text recognition in uncontrolled environments. Aggarwal’s research is distinguished by its practical orientation, aiming to bridge the gap between theoretical deep learning models and deployable systems for language processing. His efforts contribute to the broader goal of making digital information universally accessible, particularly in linguistically diverse regions. Through his focused investigations, Aggarwal continues to advance the field of computational linguistics, offering insights that inspire further innovation in transliteration and multilingual AI systems.