Accurate Fine-grained Layout Analysis for the Historical Tibetan Document Based on the Instance Segmentation
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by
Penghai Zhao, Weilan Wang, Zhengqi Cai, Guowei Zhang, Yuqi Lu
2021
Abstract
Accurate layout analysis without subsequent text-line segmentation remains an
ongoing challenge, especially when facing the Kangyur, a kind of historical
Tibetan document featuring considerable touching components and mottled
background. Aiming at identifying different regions in document images, layout
analysis is indispensable for subsequent procedures such as character
recognition. However, there was only a little research being carried out to
perform line-level layout analysis which failed to deal with the Kangyur. To
obtain the optimal results, a fine-grained sub-line level layout analysis
approach is presented. Firstly, we introduced an accelerated method to build
the dataset which is dynamic and reliable. Secondly, enhancement had been made
to the SOLOv2 according to the characteristics of the Kangyur. Then, we fed the
enhanced SOLOv2 with the prepared annotation file during the training phase.
Once the network is trained, instances of the text line, sentence, and titles
can be segmented and identified during the inference stage. The experimental
results show that the proposed method delivers a decent 72.7% AP on our
dataset. In general, this preliminary research provides insights into the
fine-grained sub-line level layout analysis and testifies the SOLOv2-based
approaches. We also believe that the proposed methods can be adopted on other
language documents with various layouts.
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