AI-Driven Voice Assistance for Multilingual Government Form Automation

Digital transformation of government services has substantially improved administrative efficiency; however, it has simultaneously exacerbated socio-digital exclusion among rural, elderly, linguistically diverse, and low-literacy populations. India's linguistic heterogeneity encompassing over 22 scheduled languages renders English- and Hindi-centric government portals inaccessible to a significant segment of the citizenry. This paper presents the design, development, and evaluation of an AI-driven voice assistance system for multilingual government form automation. The proposed system enables citizens to complete government forms exclusively through voice commands in their native regional language, thereby obviating the need for typing proficiency or English-language competency. The system integrates Automatic Speech Recognition (ASR), Natural Language Processing (NLP) with entity extraction, Optical Character Recognition (OCR) via PaddleOCR, multilingual translation, and Text-to-Speech (TTS) feedback within a unified browser-based pipeline. Backend services are implemented using the Flask web framework with MongoDB as the database management system. Empirical testing on representative government forms—including PM-Kisan Samman Nidhi, PAN Card Application, Economically Backward Class (EBC) Scholarship, Udyam MSME Registration, and GATE Examination Registration—demonstrates a 100% pass rate across all defined functional test cases (n=15 total test cases across the PM-Kisan and PAN Card forms), evaluated under controlled input conditions using binary pass/fail criteria. The system supports English, Hindi, and Marathi, with architectural provisions for extension to additional Indian regional languages. Results indicate that the platform is designed to reduce dependency on intermediaries such as Common Service Centre operators and minimize data entry errors; standardized metrics such as Word Error Rate (WER) and Task Success Rate (TSR) are identified as priorities for evaluation in future field trials. The system substantially improves accessibility for underserved populations. This work represents a meaningful contribution toward inclusive, citizen-centric digital governance in linguistically diverse developing nations.