## 2 Literature Reviews
### Development of a Process Model
#### 3.1.1 Extracting the Vector Information  
The process of extracting vector information, as depicted in Figure 1's Activity 1, utilizes sketch information provided by the clients as input and outputs vector information used for generating building components, such as walls from the sketch information. Given that the sketch information is in an analog format using paper and pen, it was converted into a digital image format through digital image acquisition portable devices, such as smartphones. Smartphones were employed for their ease of converting sketch information into a digital format, compared to more complex devices, such as scanning equipment and Sensors following the LiDAR technology. The digitally converted sketch information (sketch image) includes not only the sketch strokes that represent the centerline information of walls and the scale bars indicating scale information but also unnecessary elements, such as shadows, reflections, and the desk surface. To efficiently extract vector information used for creating building components from the sketch information, image preprocessing was conducted to remove these unnecessary details from the sketch image. The image preprocessing utilized techniques including (1) a perspective transformation algorithm, (2) a sketch information recognition model, and (3) a thinning algorithm. Through the perspective transformation algorithm, the selected area within the sketch image was transformed into a predefined rectangular shape, effectively removing unneces  
Figure 1: The process model outlines the comprehensive workflow of the framework. sary information, such as the desk surface, existing outside the paper boundary. To select the internal area of the paper for perspective transformation within the sketch image, a set of rules for choosing the paper's outer corner points was defined. The sketch information recognition model identified sketch strokes within the sketch image and eliminated unnecessary elements such as shadows and reflections inside the paper, excluding the sketch strokes. The implementation of the sketch information recognition model utilized the Pytorch 1.8.2+cu111 [14] deep learning framework, and a thinning algorithm was applied to reduce the thickness of the sketch strokes to a single pixel in the sketch image. Pytorch, a scientific computing and deep learning library, accelerates computations via GPUs, potentially speeding up operations by up to 50 times compared to CPUs. It also offers models for numerical optimization, streamlining the development of deep learning models. This preprocessing step was performed to efficiently extract the centerline information of walls from the sketch strokes. The preprocessed sketch image was then separated into images of the wall centerlines and scale bars, each saved as individual image files. The centerline image of the wall was created to extract the central line information from individual sketch strokes, and the scale bar image was generated to adjust the centerline information to the actual lengths intended by the user, based on which dimension information was calculated. To extract the centerline information used for generating building components from the centerline images of walls, a deep learning-based line detection model, LETR [15], was employed. The LETR model detected the centerline information (vector information) from individual sketch strokes (pixel information) in the input centerline images of walls. The extracted centerline information was converted into a Scalable Vector Graphics (SVG) file format, and the vertical and horizontal resolution of the SVG files was adjusted to transform the lengths to those actually intended by the client. The specific values for adjusting the image resolution were calculated using the predefined scale information (1:50) and the width of the scale bar in the scale bar image (34 pixels).