International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
AI-POWERED PETITION TRACKING AND GRIEVANCE SYSTEM Prof. R. Karthikeyan1, R Suresh Kumar 2, R Dinesh 3, R Nantha Kumar 4, G SathishKumar 5 1Assistant Professor, Department of Computer Science and Engineering,
Government College of Engineering Srirangam, Tamil Nadu, India
2,3,4,5UG Student, Department of Computer Science and Engineering,
Government College of Engineering Srirangam, Tamil Nadu, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In large institutions and government bodies, the
This paper presents the design and implementation of an AIpowered Petition Tracking and Grievance System that aims to automate the entire life cycle of a petition— from submission to resolution. The system provides users with an intuitive web interface to file complaints in text, image, or multimedia formats. A powerful OCR module extracts relevant content from scanned or handwritten documents, while ML models such as Random Forest and XGBoost classify the petitions into appropriate departments based on the content. Further, the system incorporates urgency and repetition detection to prioritize critical grievances and avoid redundant processing.
management of public grievances is a challenging task due to high volume, redundancy, delays, and lack of proper tracking mechanisms. This paper proposes a smart solution an AIpowered Petition Tracking and Grievance System designed to streamline the complaint-handling process using cutting-edge technologies. By integrating OCR, Natural Language Processing (NLP), and machine learning models, this system automates the classification of petitions, prioritizes urgent grievances, detects repetitive complaints, and ensures realtime tracking of resolution status. The system is capable of multilingual interactions through an integrated chatbot and provides actionable insights to authorities, enabling proactive governance.
One of the unique features of this system is its multilingual chatbot, designed to assist users in multiple languages, thereby ensuring inclusivity for users from diverse linguistic backgrounds. The chatbot not only helps users navigate the system but also facilitates conversational filing and tracking of petitions. Through real-time status updates and automated notifications, the system maintains a transparent line of communication between users and administrative personnel. Additionally, department-wise analytics and dashboards provide officials with actionable insights to improve internal processes and responsiveness. The backend is built using Node.js, with a robust and scalable NoSQL database managed via MongoDB, and file storage supported on AWS S3. The frontend is developed using modern web technologies to provide a seamless user experience. Supporting libraries such as OpenCV for image preprocessing and Librosa/FFmpeg for multimedia handling make the system versatile in accepting various complaint formats.
Keywords: Petition Automation, Grievance Redressal, OCR, NLP, Machine Learning, Complaint Classification, AI Chatbot, Public Service Transparency.
1.INTRODUCTION The Grievance redressal systems form a critical component of governance, whether in educational institutions, governmental departments, or corporate organizations. Efficient handling of public complaints and feedback not only strengthens the trust of stakeholders but also ensures transparency and accountability Traditional grievance redressal methods, however, are often hampered by manual processes, lack of proper documentation, delayed responses, and ineffective tracking mechanisms. These limitations create bottlenecks in addressing issues in a timely and structured manner, leading to dissatisfaction among the stakeholders and inefficiencies within the system. With the advent of Artificial Intelligence (AI), there is an emerging opportunity to automate and optimize various administrative processes, including petition management and grievance handling. AI technologies such as Machine Learning (ML), Natural Language Processing (NLP), Optical Character Recognition (OCR), and chatbot systems have demonstrated immense potential in solving real-world problems involving large-scale unstructured data, decisionmaking, and interaction handling. Leveraging these technologies in a grievance redressal system can substantially reduce the dependency on manual intervention, minimize delays, and enhance service delivery.
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Impact Factor value: 8.315
Overall, the proposed solution demonstrates how AI can transform traditional petition handling into a modern, efficient, and scalable system. The system not only addresses present-day limitations but also lays a foundation for future enhancements, including deep learning-based handwriting recognition, multilingual translation capabilities, and advanced sentiment analysis. This research showcases the practical application of AI in public service technology and contributes toward building smarter and more responsive governance models .
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