# Personalized Contextual Information Delivery Using Road Sign Recognition
## 1 Introduction  
The rapid advancements in artificial intelligence (AI) and computer vision have profoundly impacted various industries, with road sign recognition emerging as a critical application in domains such as autonomous driving, navigation systems, and traffic management. Road signs serve as essential tools for conveying directions, distances, and regulatory information to drivers. However, traditional recognition systems often fall short of delivering intuitive and user-centric insights. These systems primarily focus on object detection or basic text extraction, which limits their ability to address the contextual needs of users. For example, road signs often contain multiple objects and text components (Figure 1), yet traditional models process these elements in isolation without integrating them into meaningful, user-adapted outputs. Conventional object detection models, such as Region-Based Convolutional Neural Network (Faster-RCNN) and You Only Look Once (YOLO), are adept at identifying objects and their locations but are inherently limited in providing actionable, real-time information tailored to the user's specific context [1, 2, 3]. Advances such as YOLOv4 have enhanced speed and accuracy in real-time applications [4]. Similarly, image captioning models are designed to generate textual descriptions of images but fail to incorporate critical user-related factors such as location, destination, or driving conditions [5, 6]. These limitationshinder their applicability in scenarios requiring personalized and context-aware decision making, particularly in dynamic environments like road navigation. To address these shortcomings, this study proposes a novel approach that integrates road sign recognition with contextual information delivery. The system combines modular object detection techniques with user-specific contextual data to generate intuitive outputs that enhance usability. Leveraging advanced models such as Faster-RCNN [1], DeepLabV3 [7], and Character Region Awareness for Text Detection (CRAFT) [8], the proposed system processes road sign images to extract meaningful textual and symbolic information while incorporating user-specific details like location, speed, and destination. Furthermore, the system utilizes step-by-step reasoning to link these data points, providing actionable insights tailored to the user's immediate needs. This research contributes to the field by demonstrating a robust system capable of overcoming the limitations of traditional recognition models, paving the way for broader applications in navigation, logistics, and beyond. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Source Image** & **Contextual Data** & **Extracted Data from Image Model** \\ \hline \multirow{4}{*}{[27, 24, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,  
Recent advancements in traffic sign recognition have explored various deep learning-based approaches to improve detection and interpretation accuracy. YOLO-based models, such as Sign-YOLO [9], have integrated attention mechanisms to enhance traffic sign detection, particularly for real-time applications in autonomous driving and intelligent driver assistance systems. Similarly, Large Language Model (LLM)-driven contextual reasoning models [10] have demonstrated promising results in improving cross-domain adaptability for traffic sign recognition through few-shot in-context learning. These approaches highlight the importance of integrating context-aware reasoning into traffic sign recognition, aligning with our study's objective of enhancing usability and decision-making efficiency. This study proposes a modular system that integrates object detection, text recognition, and user-adapted contextual reasoning to improve road sign recognition. Unlike conventional approaches that only extract textual information, the proposed system transforms raw detections into meaningful insights tailored for real-world navigation.