## 3 Method  
### Dataset and Data Preparation  
The dataset used in this study was provided by the Occupational Therapy Department of National Taiwan University. This dataset contains hand-drawn sketch data from preschool children aged 3 to 6 years old and includes 12 types of drawings from the Beery VMI test. Each hand-drawn answer is saved as an image, totaling 8610 images of children's hand-drawn answers. The distribution of these images across categories is uneven, depending on the difficulty of the different shapes. For example, drawing straight lines is relatively simple for most children, so the number of images with \"incorrect straight lines\" is much smaller compared to the number of images with \"correct straight lines\". A task is classified into different categories based on the number of rules met. For instance, if a task has N rules, it will have N + 1 categories (meeting 0 to N conditions). Overall, the data distribution among different categories is imbalanced. According to the classification of error rules, the category with the fewest original images has 16 images, while the category with the most has 800 images, resulting in a 50-fold difference. Therefore, it is necessary to balance the data volume for each category through data augmentation methods.  
To augment the data for each category, this study employs computer vision techniques (OpenCV), including rotation, stretching, scaling, and skewing. It is important to note that rules impose significant restrictions on shape variations. For example, when using rotation for augmentation, it must be considered that some tasks will not be scored if horizontal lines form an angle greater than 15 degrees with the horizontal axis, while other tasks can be rotated arbitrarily without concern for the angle. When extending or shortening length, it must be ensured that some rules stipulate that line lengths must not be less than 1/16 inch. Additionally, shape distortions will change the aspect ratio, so it is important to adhere to rules that restrict the aspect ratio to less than 2:1. Furthermore, OpenCV was used to supplement parts of the drawings that were not completed according to the rules. Through these methods, a dataset of 20,365 images was ultimately generated. When separating training and validation datasets, 1/10 of the original images was randomly selected as the validation dataset. The selected validation data were not included in the training dataset, and the training dataset does not contain any augmented data based on images selected for the validation set. Augmented data were used exclusively for the training dataset and were not included in the validation dataset.  
The children's hand-drawn answers were scanned into PDF files and underwent a series of processing steps, including segmentation, cropping, binarization, denoising, and compression, and were ultimately saved at a resolution of 500 \(\times\) 500 pixels. During training, these images were resized to 224 \(\times\) 224 pixels to meet the model's input requirements. After inputting at 224 \(\times\) 224 pixels, the pixels were not adjusted during channel slicing and combining, so the final channels remained at 224 \(\times\) 224. Figure 2 uses question 11 as an example to show the difference between an image before and after augmentation.  
Figure 2: Question 11. (**a**) Before augmentation after processing and (**b**) after augmentation.