# Personalized Contextual Information Delivery Using Road Sign Recognition
## 5 Conclusions  
The proposed system demonstrates a robust and effective approach to road sign recognition, addressing the key limitations of traditional methods. By employing a modular architecture that integrates Faster-RCNN, CRAFT, and DeepLabV3, the system excels in detecting, recognizing, and extracting directional information from road sign images, achieving high performance across standard metrics such as Precision, Recall, and IOU. The use of the Chain of Thought framework for contextual information integration further enhances the system's ability to generate user-adapted and actionable insights, making \begin{table}
\begin{tabular}{l c c} \hline \hline
**System Data** & **Image Captioning Output** & **Relevant** \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & 27 km remaining to reach your destination & Highly \\  & Arrive in Mkopo in approximately 27 min & Highly \\  & There is an IIIlo IC, which is approximately 14 km & Moderate \\  & away; this could be your next exit & \\  & 27 km left to Mkopo indicate that you are nearing & Highly \\  & The road number “15” is consistent throughout & Moderate \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & As you drive at 50 km/h, you may need to reduce & \\  & speed when approaching Changhyeon & Highly \\  & Intersection & \\  & Paldong and Deokso as your next destinations on & Highly \\  & As Changhyeon Intersection is your current & Highly \\  & location, check for traffic or any signals & Highly \\  & Deokso is your right, indicating that it is in the & Highly \\  & same direction as Paldong & \\  & Based on the signs, it appears that Paldong is & Highly \\  & located to your right & \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & You are heading toward Gangneung, which is & \\  & indicated to be on your right & Highly \\  & You are currently driving at 50 km/h on road & Moderate \\  & number 456 & Moderate \\  & “Daegwallyeong” is also on your right, & Moderate \\  & Hoengye IC to your left indicates an alternate & \\  & route, so if traffic conditions worsen, that may be & Highly \\  & an option & \\  & be prepared to adjust speed as you approach & Moderate \\  & intersections & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result of the tailored vision system. it well-suited for real-world applications like navigation and traffic management. Compared to traditional image captioning models, which provide static textual descriptions, the proposed system effectively combines detected objects with user-specific data, such as location, speed, and destination, to deliver intuitive outputs tailored to the user's immediate needs. Although this study focuses on the integration of image-based contextual information with LLMs, real-time processing is a critical factor for deployment in actual road environments. However, direct comparisons of computational performance between image captioning models and CoT-based methods are challenging, as our approach pre-processes extracted data before applying LLMs with user-adapted information. Future research will explore optimization techniques, such as model compression and parallel processing, to improve real-time applicability. While the system demonstrates robust performance under diverse conditions, challenges remain, such as handling text-symbol overlaps and extending evaluations to extreme environmental conditions like heavy rain or poor lighting. Future work will focus on improving robustness in these scenarios, incorporating additional contextual data such as real-time traffic or weather information, and optimizing real-time processing capabilities to enhance the system's practicality. With these advancements, the proposed system holds significant potential for broader applications in navigation, logistics, and smart traffic management. Conceptualization, methodology, software, data analysis, and writing--original draft preparation were all performed by B.K. Supervision and project administration were handled by the corresponding author, Y.S.