Image-Based Complaint Management System for Educational Institutions

Author

Mrs. Gorisetty Nirosha, Gayathri Mandangi


Abstract

 Educational institutions regularly experience maintenance problems such as damaged furniture, electrical faults, water leakage, plumbing issues and computer-laboratory equipment failures. Conventional complaint handling often depends on verbal communication, paper forms or text-only submissions. These approaches can delay reporting, make the actual fault difficult to understand, and provide limited visibility into repair progress. This paper presents an image-based complaint management system designed for college facilities. Students submit a photograph of the issue together with a short description and facility location or QR-based location information. The proposed workflow uses image analysis and natural language processing (NLP) to estimate the complaint category, determine priority, and identify possible duplicate reports. Complaints are routed to the responsible department or faculty member. Maintenance staff update the repair status and upload an after-repair image, which is reviewed by faculty before closure. A web-based prototype is described using Flask, HTML, CSS, Bootstrap and SQLite. The prototype supports complaint submission, category estimation, rule-based priority assignment, possible duplicate detection, staff assignment, repair tracking, faculty verification, dashboard statistics and CSV report generation. The paper distinguishes implemented workflow features from AI capabilities that require further model training and validation; no new accuracy figures are claimed. The approach aims to improve traceability, accountability and transparency in campus maintenance.



Keywords

Image-based reporting, complaint management, educational institutions, CNN, NLP, maintenance tracking, duplicate detection, priority estimation.



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References


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