ServiceNow Research

OCR-VQGAN: Taming Text-within-Image Generation


Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generation remains unexplored. A challenging aspect of generating figures and diagrams is effectively rendering readable texts within the images. To alleviate this problem, we present OCR-VQGAN, an image encoder and decoder that leverages OCR pre-trained features to optimize a text perceptual loss, encouraging the architecture to preserve high fidelity text and diagram structure. To explore our approach, we introduce the Paper2Fig100k dataset, with over 100k images of figures and texts from research papers. The figures show architecture diagrams and methodologies of articles available at from fields like artificial intelligence and computer vision. Figures usually include text and discrete objects, e.g., boxes in a diagram, with lines and arrows that connect them. We demonstrate the superiority of our method by conducting several experiments on the task of figure reconstruction. Additionally, we explore the qualitative and quantitative impact of weighting different perceptual metrics in the overall loss function.

Winter Conference on Applications of Computer Vision (WACV)
David Vazquez
David Vazquez
Manager of Research Programs

Manager of Research Programs at Research Management located at Montreal, QC, Canada.

Issam H. Laradji
Issam H. Laradji
Research Scientist

Research Scientist at Low Data Learning located at Vancouver, BC, Canada.