Deep imitator: Handwriting calligraphy imitation via deep attention networks
作者：
Highlights：
• Handwriting style features are extracted by a Convolution Neural Network.
• Calligraphy embedding is computed by attention and orthogonal mata-style matrix.
• Calligraphy and character embedding are the inputs of Dual-condition Gated Recurrent Unit.
• Handwriting Imitation results are generated by Gaussian Mixture Model and sampling.
• Subjective and quantitative tests verify the imitation performance.
摘要
•Handwriting style features are extracted by a Convolution Neural Network.•Calligraphy embedding is computed by attention and orthogonal mata-style matrix.•Calligraphy and character embedding are the inputs of Dual-condition Gated Recurrent Unit.•Handwriting Imitation results are generated by Gaussian Mixture Model and sampling.•Subjective and quantitative tests verify the imitation performance.
论文关键词：Calligraphy imitation,Attention,Mata-style matrix,Condition gated recurrent unit
论文评审过程：Received 20 February 2019, Revised 8 September 2019, Accepted 12 October 2019, Available online 3 February 2020, Version of Record 24 April 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107080
