Writer identification using curvature-free features
作者：
Highlights：
• We propose two novel and curvature-free features: LBPruns and COLD features for writer identification.
• The LBPruns is the joint distribution of run-length and local binary pattern.
• The COLD is the joint distribution of the relation between orientations and lengths of line segments.
• The combination of the LBPruns and COLD features provides a significant improvement on the curvature-less CERUG data set.
摘要
Highlights•We propose two novel and curvature-free features: LBPruns and COLD features for writer identification.•The LBPruns is the joint distribution of run-length and local binary pattern.•The COLD is the joint distribution of the relation between orientations and lengths of line segments.•The combination of the LBPruns and COLD features provides a significant improvement on the curvature-less CERUG data set.
论文关键词：Writer identification,Curvature-free,Run-lengths of local binary pattern,Cloud of line distribution
论文评审过程：Received 12 February 2016, Revised 22 September 2016, Accepted 25 September 2016, Available online 28 September 2016, Version of Record 10 November 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.044
