NeurTEx: A Neural Framework for Template Extraction from Flat Images

BSTRACT With the increasing demands of digital creation, a creative user often starts by looking for inspirational resources online. Once they find such inspirations, they recreate certain design elements using tracing-like applications to internalize the design for their purpose. We aim to accelerate this process by extracting key semantics from an inspirational image, and converting it into a ready-to-use template using a multimodal information extraction setup. We propose NeurTEx, a holistic algorithm that takes an inspirational banner image as input and extracts multimodal design semantics: layout, text elements (actual text along with the font style), image elements (including semantics like logos and background/foreground objects) and shapes. Our technique uses a segmentation framework followed by a region based depth-first search to extract and identify different elements and their semantics. We process these regions to extract finer details such as font styles, texts and logos. We believe that such fine–grained semantics would accelerate the design process for a creator helping them rapidly adapt designs from inspirations for their requirements. With the help of metric-based and human survey based evaluations, we not only demonstrate the effectiveness of the proposed approach to extract the style & design components from an inspirational image but also illustrate how these extractions can accelerate the creation process thus aiding novices and professionals alike.