# Personalized Contextual Information Delivery Using Road Sign Recognition
## 3 Proposed System
### Modular Components for Road Sign Recognition
#### 3.2.1 Object and Text Detection in Road Sign Images  
Object detection is the foundational step of the proposed system, focusing on identifying and localizing textual and symbolic elements within road sign images. Faster-RCNN is employed for this task due to its ability to achieve high accuracy in detecting small and overlapping objects. The model's two-stage process involves generating candidate regions using a Region Proposal Network (RPN) and refining these proposals through classification and bounding box regression [1; 2]. Figure 4 presents examples of data acquired by the text recognition model, illustrating detected text regions and individual recognized text elements.  
Figure 3: Road sign data augmentation using road sign object information. Each box represents a synthetic road sign generated for data augmentation purposes, and the internal texts and symbols within the boxes represent individual road sign objects extracted from real-world road signs in the Republic of Korea (See Figure 1 for additional context regarding Korean text).  
To effectively handle the diverse characteristics of road sign images, the system incorporates optimized anchor box configurations and aspect ratios. These configurations were tailored to accommodate small-scale objects such as text regions and directional arrows, which are prevalent in road sign datasets. The detailed configurations and their performance improvements are presented in Section 4, where the experimental results validate the system's robustness and accuracy under various conditions [6,7].  
By leveraging Faster-RCNN's flexibility and Precision, the system ensures an accurate detection of key objects, laying the groundwork for subsequent modules, such as text recognition and contextual information integration [8].  
To ensure the accurate detection of road sign components, the Faster-RCNN model was optimized with customized anchor box configurations. Unlike conventional object detection tasks, road sign elements, such as directional arrows and text, are often small and densely packed. To address this, the anchor box sizes were refined to [8 \(\times\) 8, 16 \(\times\) 16, 32 \(\times\) 32, 64 \(\times\) 64, 128 \(\times\) 128], improving detection accuracy. Additionally, text detection and recognition benefited from data augmentation techniques such as rotation, scaling, and contrast adjustment, which enhanced recognition under varying environmental conditions.  
After identifying the road signs using object detection, the next essential step is extracting textual information directly from these detected objects. Therefore, our system integrates object detection with robust text detection and optical character recognition (OCR), ensuring accurate and contextually relevant results.  
Following object detection, the system focuses on identifying and recognizing text within road sign images. The CRAFT model was selected for its ability to detect text regions with high Precision, even in complex layouts and orientations [8,11]. CRAFT identifies character-level features, which are particularly effective for road signs containing multilingual text.  
Once text regions are detected, OCR techniques are applied to convert them into machine-readable formats. To enhance the model's robustness, the training dataset included a combination of Korean and English text, reflecting the diversity of road signs. Data augmentation techniques such as rotation, scaling, brightness adjustment, and noise addition were employed to simulate real-world scenarios. These efforts significantly improved the model's performance under various lighting conditions and environmental complexities [12,13].