Corpus ID: 235828717

Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data

  title={Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data},
  author={Christian Bartz and Hendrik R{\"a}tz and Haojin Yang and Joseph Bethge and Christoph Meinel},
One of the most pressing problems in the automated analysis of historical documents is the availability of annotated training data. In this paper, we propose a novel method for the synthesis of training data for semantic segmentation of document images. We utilize clusters found in intermediate features of a StyleGAN generator for the synthesis of RGB and label images at the same time. Our model can be applied to any dataset of scanned documents without the need for manual annotation of… Expand

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