@techreport{oai:ipsj.ixsq.nii.ac.jp:00191373,
 author = {Yujung, Wang and Motoharu, Sonogashira and Atsushi, Hashimoto and Masaaki, Iiyama and Yujung, Wang and Motoharu, Sonogashira and Atsushi, Hashimoto and Masaaki, Iiyama},
 issue = {28},
 month = {Sep},
 note = {In this paper, we propose a method to recover strokes from offline handwritten Chinese characters. The proposed method employs multitask fully convolutional networks (FCN) to estimate writing order and direction of strokes from offline character images. Online data in CASIA database is used for training. Since the FCN produces discontinuous strokes, our method refines the estimation of the writing order by using a graph cut (GC). Experimental results with test dataset of CASIA-OLHWDB1.0 show the effectiveness of our method., In this paper, we propose a method to recover strokes from offline handwritten Chinese characters. The proposed method employs multitask fully convolutional networks (FCN) to estimate writing order and direction of strokes from offline character images. Online data in CASIA database is used for training. Since the FCN produces discontinuous strokes, our method refines the estimation of the writing order by using a graph cut (GC). Experimental results with test dataset of CASIA-OLHWDB1.0 show the effectiveness of our method.},
 title = {Stroke Recovery of Handwritten Chinese Character using Fully Convolutional Networks},
 year = {2018}
}