Graph-based Deep Generative Modelling for Document Layout Generation
One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
Code (0)
등록된 구현이 없습니다.
Tasks
Layout GenerationSimilar Papers 제목 키워드 기반
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator
Layouts are important for graphic design and scene generation. We propose a novel generative adversarial network, named as LayoutGAN, that synthesizes graphic layouts by modeling semantic and geometric relations of 2D el…
Generative Adversarial NetworkLayout GenerationScene GenerationLayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements…
Generative Adversarial NetworkLayout GenerationScene GenerationOmniDocLayout: Towards Diverse Document Layout Generation via Coarse-to-Fine LLM Learning
Document AI has advanced rapidly and is attracting increasing attention. Yet, while most efforts have focused on document layout analysis (DLA), its generative counterpart, layout generation, remains underexplored. Disti…
Document Layout AnalysisDocument AIConstrained Graphic Layout Generation via Latent Optimization
It is common in graphic design humans visually arrange various elements according to their design intent and semantics. For example, a title text almost always appears on top of other elements in a document. In this work…
Layout GenerationDiverse Multimedia Layout Generation with Multi Choice Learning
Designing visually appealing layouts for multimedia documents containing text, graphs and images requires a form of creative intelligence. Modelling the generation of layouts has recently gained attention due to its impo…
Layout Generation