## 1 Introduction  
The rise of data-driven techniques, particularly deep learning, has made them the leading approach for solving computer vision problems in various fields. The collection of larger, more complicated datasets is necessary for supervised machine learning, and this comes at a cost. How long does it take to manually annotate the labels? How can the quality of the labels be guaranteed? How is the representativeness of the data to production data ensured? Making a large-scale synthetic dataset is an exciting new way to tackle this challenge, especially for object detection applications. Obtaining the labeled datasets required to train machine learning models is made easier with the use of synthetic data. We believe that synthetic datasets will soon be revolutionary because research in artificial intelligence and GPUs is advancing rapidly, and the world is becoming data-driven. Another motivation to develop 3D traffic sign boards was inspired by an excellent procedural generation pipeline. BlenderProc [1] generates synthetic dataset labels such as bounding boxes, segmentation, depth, normal maps, etc. Using open-source Blender 3D software [2],BlenderProc allows us to easily use high-level APIs to extract images and labels from the 3D scene inside the blender environment if we have the 3D models ready. We build 3D models in the Blender 3D program, which has Python APIs that are useful in automating the process, as opposed to Unity [3], which uses C++ plugins. Any operation performed in the Blender GUI can be replicated using the Python API equivalent. Synthetic datasets have emerged as valuable resources for training and evaluating object detection models, offering controlled environments with diverse scenarios [4] and conditions that may be encountered in real-world settings [5]. These datasets enable researchers to simulate complex real-world scenarios, including variations in lighting and weather, which are crucial for developing robust and reliable object detection systems. However, these datasets are from video games, making them suitable for general object detection and semantic segmentation frameworks. But these cannot be used when the user needs a custom requirement to differentiate meshes within the 3D models as attributes like text characters. [PERSON] [6] proposed a method to effectively detect text area by exploring each character and the affinity between characters to overcome the lack of individual character-level annotations. Since we already have the individual character annotations, our problem is slightly different when we try to group words from the individual character predictions. This requires an innovative solution where the extraction of meaningful information from the 3D models can be achieved efficiently. We developed a two-stage model where the first stage detects the objects and texts, and the second stage is used to perform post-processing and recognize the texts. We believe that our 3D synthetic dataset for traffic sign boards is the first to be implemented and is powerful with multiple objects in a single scene, similar to the Microsoft Common Objects in Context (MS COCO) [7] dataset, which has various classes including attributes within the sign board such as English and Kanji text characters, numeric characters, and directional arrow marks. Also, the need for domain adaptation for adapting between synthetic and real data and adapting between normal and adverse weather cases can be eliminated if the 3D models are realistic and robust enough. The crucial problem of object identification in real-world situations has numerous applications in disciplines including robotics, augmented reality, and autonomous driving. Conventional object detection techniques frequently depend on anchor-based models, which locate and categorize items within images using pre-established bounding boxes [8; 9].