Intelligent Parsing: An Automated Parsing Framework for Extracting
  Design Semantics from E-commerce Creatives
Guandong Li Xian Yang
In the industrial e-commerce landscape, creative designs such as banners and
posters are ubiquitous. Extracting structured semantic information from
creative e-commerce design materials (manuscripts crafted by designers) to
obtain design semantics represents a core challenge in the realm of intelligent
design. In this paper, we propose a comprehensive automated framework for
intelligently parsing creative materials. This framework comprises material
recognition, preprocess, smartname, and label layers. The material recognition
layer consolidates various detection and recognition interfaces, covering
business aspects including detection of auxiliary areas within creative
materials and layer-level detection, alongside label identification.
Algorithmically, it encompasses a variety of coarse-to-fine methods such as
Cascade RCNN, GFL, and other models. The preprocess layer involves filtering
creative layers and grading creative materials. The smartname layer achieves
intelligent naming for creative materials, while the label layer covers
multi-level tagging for creative materials, enabling tagging at different
hierarchical levels. Intelligent parsing constitutes a complete parsing
framework that significantly aids downstream processes such as intelligent
creation, creative optimization, and material library construction. Within the
practical business applications at Suning, it markedly enhances the exposure,
circulation, and click-through rates of creative materials, expediting the
closed-loop production of creative materials and yielding substantial benefits.