Fully Convolutional Network Based Skeletonization for Handwritten Chinese Characters | |
Wang TQ(王铁强)2,3; Liu CL(刘成林)2,3 | |
2018 | |
会议日期 | 2018-2 |
会议地点 | 美国,新奥尔良 |
英文摘要 | Structural analysis of handwritten characters relies heavily on robust skeletonization of strokes, which has not been solved well by previous thinning methods. This paper presents an effective fully convolutional network (FCN) to extract stroke skeletons for handwritten Chinese characters. We combine the holistically-nested architecture with regressive dense upsampling convolution (rDUC) and recently proposed hybrid dilated convolution (HDC) to generate pixel-level prediction for skeleton extraction. We evaluate our method on character images synthesized from the online handwritten dataset CASIA-OLHWDB and achieve higher accuracy of skeleton pixel detection than traditional thinning algorithms. We also conduct skeleton based character recognition experiments using convolutional neural network (CNN) classifiers on offline/online handwritten datasets, and obtained comparable accuracies with recognition on original character images. This implies the skeletonization loses little shape information. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44413] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
通讯作者 | Liu CL(刘成林) |
作者单位 | 1.CAS Center for Excellence of Brain Science and Intelligence Technology 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wang TQ,Liu CL. Fully Convolutional Network Based Skeletonization for Handwritten Chinese Characters[C]. 见:. 美国,新奥尔良. 2018-2. |
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