Automated Car Insurance Claim System using OCR and ResNeXt

the increasing incidence of vehicle accidents emphasizes the need for faster and more efficient claims handling, as well as methods for categorizing damage. The proposed system addresses key challenges of traditional methods, such as excessive paperwork, manual data entry, and delayed processing times, by integrating advanced technologies including Optical Character Recognition (OCR), machine learning, and real-time analytics. This study evaluates a model for categorizing auto damage, focusing on dents and glass breaks, and introduces an innovative approach to streamline car insurance claims through Automated Verification. Policyholders can seamlessly initiate claims using mobile devices to capture images of damaged vehicle parts and license plates. OCR technology extracts and verifies textual information from these images and related documents, while Convolutional Neural Networks (CNN) are utilized for real-time object detection, enabling accurate identification and classification of vehicle damage. Additionally, machine learning models enhance the system's capability to differentiate between legitimate and fraudulent claims, thereby reducing operational costs and minimizing risks. This integrated approach significantly accelerates claim resolution, improves operational efficiency, and enhances customer experience. By fostering a responsive, automated, and user-centric ecosystem, this solution sets a new standard for innovation and effectiveness in the car insurance sector.