In the field of computer graphics, the use of vector graphics, particularly
Scalable Vector Graphics (SVG), represents a notable development from
traditional pixel-based imagery. SVGs, with their XML-based format, are
distinct in their ability to directly and explicitly represent visual elements
such as shape, color, and path. This direct representation facilitates a more
accurate and logical depiction of graphical elements, enhancing reasoning and
interpretability. Recognizing the potential of SVGs, the machine learning
community has introduced multiple methods for image vectorization. However,
transforming images into SVG format while retaining the relational properties
and context of the original scene remains a key challenge. Most vectorization
methods often yield SVGs that are overly complex and not easily interpretable.
In response to this challenge, we introduce our method, Simple-SVG-Generation
(S\textsuperscript{2}VG\textsuperscript{2}). Our method focuses on producing
SVGs that are both accurate and simple, aligning with human readability and
understanding. With simple images, we evaluate our method with reasoning tasks
together with advanced language models, the results show a clear improvement
over previous SVG generation methods. We also conducted surveys for human
evaluation on the readability of our generated SVGs, the results also favor our
methods.

通过引入我们的方法（Simple-SVG-Generation，简称 S	extsuperscript {2} VG	extsuperscript {2}），我们专注于生成准确且简单的 SVG 图像，与人类的可读性和理解力相一致，通过与先进的语言模型一起进行推理任务的简单图像评估，结果显示与先前的 SVG 生成方法相比有明显的改进。我们还对生成的 SVG 可读性进行了人类评估的调查，结果也对我们的方法持利好态度。