Choose What You Need: Disentangled Representation Learning for Scene Text Recognition Removal and Editing
Authors: Boqiang Zhang, Hongtao Xie, Zuan Gao, Yuxin Wang

Scene text images contain not only style information (font background) but also content information (character texture). Different scene text tasks need different information but previous representation learning methods use tightly coupled features for all tasks resulting in sub-optimal performance. We propose a Disentangled Representation Learning framework (DARLING) aimed at disentangling these two types of features for improved adaptability in better addressing various downstream tasks (choose what you really need). Specifically we synthesize a dataset of image pairs with identical style but different content. Based on the dataset we decouple the two types of features by the supervision design. Clearly we directly split the visual representation into style and content features the content features are supervised by a text recognition loss while an alignment loss aligns the style features in the image pairs. Then style features are employed in reconstructing the counterpart image via an image decoder with a prompt that indicates the counterpart's content. Such an operation effectively decouples the features based on their distinctive properties. To the best of our knowledge this is the first time in the field of scene text that disentangles the inherent properties of the text images. Our method achieves state-of-the-art performance in Scene Text Recognition Removal and Editing.