## 2 Literature Reviews
### Implementation of the Framework
#### 3.2.1 Implementation of Extracting Vector Information System
Each individual Residual unit in the encoding structure was progressively connected to each Residual unit in the decoding structure through skip connections. Each Residual unit performed operations combining a 3 \(\times\) 3 convolution layer, batch normalization, and ReLU activation function, followed by an addition operation with the initial input variable, \(x\). The Pytorch deep learning framework and the Python programming language were used to implement the sketch information recognition model. For training the model, a dataset of 50 images with a resolution of 224 pixels in width and height was created, consisting of input images and mask images. The input images used were perspective-transformed sketch images, and the mask images were used to represent images with sketch strokes. Choosing the right loss and optimization functions is essential for a deep learning model to effectively optimize parameters and improve accuracy. For a sketch recognition model, which aims to classify pixels in a sketch as either strokes or not, the Binary Cross Entropy loss function was selected for its relevance to binary classification problems. This function measures the model's output against the true segmentation. Given the non-linear and complex nature of sketch information, which includes variations in spatial requirements, pen thickness, and the effect of hand tremor, the Adam [22] optimizer was used to efficiently adjust the model's parameters. Both components were implemented via Pytorch. The number of training epochs was set to 150, and the batch size was set to 16. Table 1, column 2 \"Remove noises from inside the sheet\", illustrates the model's ability to recognize sketch strokes (white pixels) and classify non-stroke information (black pixels) in the sketch image. A wall shape representation image (wall image) was generated using the sketch information recognition model from sketch images. To convert the thickness of sketch strokes in the wall image to a single pixel, a thinning algorithm [23] was implemented using the OpenCV library. This algorithm removed pixels not central to individual sketch strokes in the wall image. An algorithm was developed to save the thinned wall image into designated areas within the image as separate files for the scale bar image and the wall's centerline image. Table 1, in the column \"Retrieve center lines\", illustrates the application of the thinning algorithm to convert the thickness of sketch strokes to a single pixel, and the \"Split the image\" column shows the process of separating the wall image into the scale bar image and the wall's centerline image. For extracting the central line information (centerlines) of individual walls from the wall's centerline image, the LETR model by [15] was adopted. The LETR model, upon inputting an image, detects lines within the image and outputs the \(x,y\) coordinates of the start and end points of individual lines. The centerline extraction mechanism utilizing the LETR model was set in two stages: (1) extracting morphological features of neighboring pixels in the wall's centerline image, and (2) selecting detailed line objects in the wall's centerline image. The wall's centerline image was input into the LETR model with a resolution of 256 pixels in width and height, and Table 1's column \"Retrieve vector data\" displays the extraction of centerlines from the wall's centerline image using the LETR model. An algorithm was developed to save the centerlines at a resolution of 512 pixels in width and 362 pixels in height in a Scalable Vector Graphics (SVG) file format, adjusting the length of the centerlines to the length intended by the user, utilizing the OpenCV library. The adjustment of the SVG file's vertical and horizontal resolution and the length of the centerlines were calculated using the width of the white pixels in the scale bar image (34 pixels), the physical length of the scale bar represented on the paper (2 cm), and a predefined scale ratio (1/50).