Abstract
Deep generative models have been recently experimented in automated document layout generation, which led to significant qualitative results, assessed through user studies and displayed visuals. However, no reproducible quantitative evaluation has been settled in these works, which prevents scientific comparison of upcoming models with previous models. In this context, we propose a fully reproducible evaluation method and an original and efficient baseline model. Our evaluation protocol is meticulously defined in this work, and backed with an open source code available on this link: https://github.com/romain-rsr/quant_eval_for_document_layout_generation/tree/master.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Myers, B.A.: User interface software tools. ACM Trans. Comput. Hum. Interact. (1994)
Lok, S., Feiner, S., Ngai, G.: Evaluation of visual balance for automated layout. In: 9th International Conference on Intelligent User Interfaces (2004)
Feiner, S., Nagy, S., Van Dam, A.: An experimental system for creating and presenting interactive graphical documents. ACM Trans. Graph. (1982)
Merell, P., Schkufza, E., Li, Z., Agrawala, M., Koltun, V.: Interactive furniture layout using interior design guidelines. SIGGRAPH (2011)
Lin, X.: Active layout engine: algorithms and applications in variable data printing. Comput. Aided Des. (2005)
Purvis, L., Harrington, S., O’Sullivan, B., Freuder, E.: Creating personalized documents: an optimization approach. In: ACM Symposium on Document Engineering (2003)
Li, J., Yang, J., Hertzmann, A., Zhang, J., Xu, T.: LayoutGAN: generating graphic layouts with wireframe discriminators. In: ICLR (2019)
Li, J., Yang, J., Zhang, J., Liu, C., Wang, C., Xu, T.: Attribute-conditioned layout GAN for automatic graphic design. IEEE Trans. Visual. Comput. Graph. (2020)
Lee, H..-Y.., et al.: Neural design network: graphic layout generation with constraints. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 491–506. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_29
Carletto, R., Cardot, H., Ragot, N.: Automatic generation of web advertising layouts: a synthetic dataset and a deep learning baseline model. In: ICPRS (2021)
Deka, B., et al.: Rico: a mobile app dataset for building data-driven design applications. In: ACM Symposium on User Interface Software and Technology (2017)
Zheng, X., Qiao, X., Cao, Y., LAU, R.W.H.: Content-aware generative modeling of graphic design layouts. ACM Trans. Graph. (2019)
Jyothi, A.A., Durand, T., He, J., Sigal, L., Mori, G.; LayoutVAE: stochastic scene layout generation from a label set. In: ICCV (2019)
Nauata, N., Chang, K.-H., Cheng, C.-Y., Mori, G., Furukawa, Y.: House-GAN: relational generative adversarial networks for graph-constrained house layout generation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 162–177. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_10
Schroeder, B., Tripathi, S., Tang, H.: Triplet-aware scene graph embeddings. In: Scene Graph Representation Learning Workshop at ICCV (2019)
Dowson, D.C., Landau, B.V.: The fréchet distance between multivariate normal distributions. J. Multivar. Anal. (1982)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. NIPS (2017)
Acknowledgements
This work is financed by Centre Val de Loire Region, in France, and by Madmix Digital, a creative studio based in Paris and New-York, who helped us to identify and scientifically match the major challenges of document layout generation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Carletto, R., Cardot, H., Ragot, N. (2021). Deep Learning for Document Layout Generation: A First Reproducible Quantitative Evaluation and a Baseline Model. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-86334-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86333-3
Online ISBN: 978-3-030-86334-0
eBook Packages: Computer ScienceComputer Science (R0)