Oracle-bone-inscription image segmentation based on simple fully convolutional networks

Guoying Liu; Xu Song; Wenying Ge; Hongyu Zhou; Jing Lv

doi:10.1117/12.2539422

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14 February 2020 Oracle-bone-inscription image segmentation based on simple fully convolutional networks

Guoying Liu, Xu Song, Wenying Ge, Hongyu Zhou, Jing Lv

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Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301I (2020) https://doi.org/10.1117/12.2539422
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Oracle bone inscriptions (OBIs) are invaluable materials for recovering the economic and social forms for Shang Dynasty, one of the most ancient dynasties in China. It is very important to get the original OBIs from scanned images of oracle bone rubbings. To this end, researchers have to employ a very time-consuming method that they follow the inscriptions by handwritten tools, pixel by pixel and image by image. In this paper, an image segmentation method was proposed to overcome this limitation based on fully convolutional networks (FCN). In order to speed up training as well as boost the segmentation performance, a simple FCN with only convolutional layers was designed, where batch normalization was incorporated. The proposed method was tested on a real OBI image set (320 samples). Experimental results show that the proposed method is effective enough to get the OBIs from scanned images of oracle bone rubbings.

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Guoying Liu, Xu Song, Wenying Ge, Hongyu Zhou, and Jing Lv "Oracle-bone-inscription image segmentation based on simple fully convolutional networks", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301I (14 February 2020); https://doi.org/10.1117/12.2539422

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