Developing production-ready NLP systems for low-resource languages involves unique hurdles that standard pre-trained architectures cannot solve out of the box. This session provides a practical, engineering-focused look at the data pipelines required to handle noisy, informal, and morphologically complex text corpora. We will break down the structural implementation of a massive 7.19-million-sentence Wikipedia dataset pipeline, detailing the critical preprocessing stages: Unicode normalization, handling complex word formations, and token-free character tokenization. Furthermore, we will explore the architectural logic of mapping text to Indic phoneme representations to capture pronunciation-aware features. This session is a pure engineering blueprint for developers looking to prepare noisy textual data for high-performance sequence models

Session Format & Duration: 30–45 Minute Technical Session. Target Audience: Data Engineers, NLP Developers, and Software Engineers interested in data preprocessing pipelines for non-English scripts. Technical Requirements: Standard projector setup with HDMI. Presentation Outline (35 Minutes):00:00–00:10 | Data Engineering Foundation: Sourcing and structuring lexical resources, cleaning raw text, and implementing strict Unicode normalization rules. 00:10–00:25 | Pipeline Mechanics: Building character-level tokenization filters and implementing the algorithmic mapping for Indic phoneme dictionaries. 00:25–00:35 | Integration & Architecture Output: How this structured data maps into embedding tensors for deep learning models, ending with an open technical Q&A.