Personalized Contextual Information Delivery Using Road Sign Recognition

Road sign recognition is essential for navigation and autonomous driving applications. While existing models focus primarily on text detection and extraction, they fail to incorporate user-specific contextual information, limiting their effectiveness in real-world scenarios. This study proposes a modular system that enhances road sign recognition by integrating user-adapted contextual reasoning. The system applies a step-by-step Chain of Thought (CoT) approach to link detected road signs with relevant contextual data, such as location, speed, and destination. Compared to traditional image captioning models, our approach significantly improves information relevance and usability. Experimental results show that the proposed system achieves a 23.4% increase in user-adapted information accuracy and reduces interpretation errors by 17.8% in real-world navigation scenarios. These findings demonstrate that semantic inference-based reasoning improves decision-making efficiency, making road sign recognition systems more practical for real-world applications. The study also discusses challenges such as real-time processing limitations and potential future improvements for broader infrastructure recognition.