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DeepLayout: A Semantic Segmentation Approach to Page Layout Analysis

  • Yixin Li
  • Yajun Zou
  • Jinwen Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

In this paper, we present DeepLayout, a new approach to page layout analysis. Previous work divides the problem into unsupervised segmentation and classification. Instead of a step-wise method, we adopt semantic segmentation which is an end-to-end trainable deep neural network. Our proposed segmentation model takes only document image as input and predicts per pixel saliency maps. For the post-processing part, we use connected component analysis to restore the bounding boxes from the prediction map. The main contribution is that we successfully bring RLSA into our post-processing procedures to specify the boundaries. The experimental results on ICDAR2017 POD competition dataset show that our proposed page layout analysis algorithm achieves good mAP score, outperforms most of other competition participants.

Keywords

Page layout analysis Document segmentation Document image understanding Semantic segmentation and deep learning 

Notes

Acknowledgement

This work was supported by the Natural Science Foundation of China for Grant 61171138.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information Science, School of Mathematical Sciences and LMAMPeking UniversityBeijingChina

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