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2021 | OriginalPaper | Chapter

LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencoder Framework

Authors : Jie He, Xingjiao Wu, Wenxin Hu, Jing Yang

Published in: Document Analysis and Recognition – ICDAR 2021

Publisher: Springer International Publishing

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Abstract

The lack of training data is still a challenge in the Document Layout Analysis task (DLA). Synthetic data is an effective way to tackle this challenge. In this paper, we propose an LSTM-based Variational Autoencoder framework (LSTMVAF) to synthesize layouts for DLA. Compared with the previous method, our method can generate more complicated layouts and only need training data from DLA without extra annotation. We use LSTM models as basic models to learn the potential representing of class and position information of elements within a page. It is worth mentioning that we design a weight adaptation strategy to help model train faster. The experiment shows our model can generate more vivid layouts that only need a few real document pages.

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previous chapter C2VNet: A Deep Learning Framework Towards Comic Strip to Audio-Visual Scene Synthesis
next chapter HCRNN: A Novel Architecture for Fast Online Handwritten Stroke Classification
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Metadata
Title
LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencoder Framework
Authors
Jie He
Xingjiao Wu
Wenxin Hu
Jing Yang
Copyright Year
2021
Book
Document Analysis and Recognition – ICDAR 2021

Print ISBN: 978-3-030-86330-2

Electronic ISBN: 978-3-030-86331-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-86331-9_12

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