# Personalized Contextual Information Delivery Using Road Sign Recognition
## 4 Experiments and Results
### Comparative Analysis with Image Captioning Models  
The comparison between the proposed system and traditional image captioning models highlights the significant differences in their ability to generate actionable and context-aware outputs. While image captioning models are capable of producing textual descriptions of visual data, they primarily focus on static, surface-level information extracted directly from the image. This often results in outputs that require additional interpretation by the user to be meaningful in specific contexts [4; 5]. Tables 2 and 3 further illustrate this distinction by presenting a side-by-side comparison of raw text extraction and context-aware outputs. In Table 2, the extracted data consist solely of recognized text from road signs, without additional contextual reasoning. In contrast, Table 3 showcases the proposed system's ability to integrate user-specific contextual data -- such as location, speed, and destination -- with detected road sign objects. The key difference lies in the application of semantic inference, where Table 3's outputs, marked in blue, demonstrate the transformation of raw textual data into actionable insights. For example, while an image captioning model might describe a road sign as _\"A green sign with the text '27 km' and 'Mokpo\"_, this information lacks immediate utility for the user [3; 11]. Conversely, the proposed system contextualizes these data by generating outputs such as _\"At your current speed of 60 km/h, you will arrive in Mokpo in approximately 27 min\"_. These blue-labeled outputs represent information that is not only relevant but also immediately actionable and intuitive for the user. By reducing the cognitive effort required for decision making, this approach significantly enhances real-world usability [17; 18]. \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Source Image**} & \multicolumn{1}{c}{**Image Captioning Output**} & \multicolumn{1}{c}{**Relevant**} \\ \hline \hline \multicolumn{3}{c}{A close up of a street sign with a sky background} & Not \\ \multicolumn{3}{c}{A close up of a street sign with a street sign} & Not \\ \multicolumn{3}{c}{A street sign on a pole on a street} & Not \\ \multicolumn{3}{c}{Information boards with green and green signs} & Not \\ \multicolumn{3}{c}{The image shows a green road sign, indicating the} & Moderate \\ \multicolumn{3}{c}{distances to the destinations} & Not \\ \hline \hline \multicolumn{3}{c}{A close up of street sign that reads Korean} & Moderate \\ \multicolumn{3}{c}{A close up of street sign with traffic light} & Not \\ \multicolumn{3}{c}{A street sign that is on a pole} & Not \\ \multicolumn{3}{c}{A sign in English and Korean with a green sign} & Moderate \\ \multicolumn{3}{c}{above it} & The image shows a road sign with directions in \\ \multicolumn{3}{c}{Korean and English} & Moderate \\ \hline \hline \multicolumn{3}{c}{A close up of a street sign with a sky background} & Not \\ \multicolumn{3}{c}{A close up of a street sign with a sign on it} & Not \\ \multicolumn{3}{c}{A street sign on a street} & Not \\ \multicolumn{3}{c}{Signs in Korean and signs indicating there} & Not \\ \multicolumn{3}{c}{A road sign displaying directions and names of} & Moderate \\ \multicolumn{3}{c}{various locations} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result of the image captioning model. While Table 2 provides a direct textual output from road signs, Table 3 demonstrates how our approach enhances readability and usability by embedding user-adapted contextual reasoning. The text highlighted in blue in Table 3 represents instances where semantic information was inferred, further proving the superiority of our contextualization method over traditional text extraction approaches. This process ensures that extracted information is not merely descriptive but directly contributes to user-oriented decision making in real-world applications such as navigation and traffic management [8,17,18].