The core issue arises from phonetic variations, inconsistent transliterations, and data entry errors that affect the accuracy and efficiency of searching Hindi names in police databases. These inconsistencies lead to delayed investigations, misidentified individuals, and, in some cases, legal complications.Solution ApproachThe solution revolves around the development of a fuzzy name matching system optimized for law enforcement needs. Key technical components include:Phonetic and Fuzzy Matching Algorithms: Algorithms like Levenshtein distance and Soundex are employed to match names based on similarity, accounting for phonetic differences.Real-time Error Detection and Correction: The system implements real-time spell checks and suggests corrections to minimize errors during data entry.Script Interoperability: Leveraging tools like Indic-NLP, the system ensures seamless conversion between Devanagari and Roman scripts, enhancing search accuracy across languages.Transliteration Standardization: A consistent transliteration system is established to prevent confusion caused by varied spellings of Hindi names in Roman script.Technical ArchitectureThe technical solution integrates several modern tools and frameworks:Kotlin for core logic and data processing.Jetpack Compose for building the user interface.Firebase for backend services, including authentication, real-time databases, and cloud messaging.Scikit-learn for training AI models used in fuzzy matching.Figma for user interface and experience design.Impact and BenefitsThe proposed solution promises significant improvements in data accuracy, reducing errors in name retrieval and record-keeping. It enhances search efficiency, leading to faster investigations by law enforcement agencies. The system also supports both Devanagari and Roman scripts, improving usability across different linguistic backgrounds. Moreover, by incorporating real-time error correction and phonetic search capabilities, the tool ensures better handling of name variations, minimizing legal risks and enhancing overall data quality.