Gender classification from offline multi-script handwriting images using oriented Basic Image Features (oBIFs)

作者：

Highlights：

• Characterization of gender from handwriting using oriented Basic Image Features.

• Investigation of multiple configurations of oriented Basic Image Features.

• Comprehensive series of experiments on a standard dataset.

• Enhanced classification rates when compared to state-of-the-art techniques.

摘要

•Characterization of gender from handwriting using oriented Basic Image Features.•Investigation of multiple configurations of oriented Basic Image Features.•Comprehensive series of experiments on a standard dataset.•Enhanced classification rates when compared to state-of-the-art techniques.

论文关键词：Gender classification,oBIFs histogram,oBIFs columns histogram,QUWI database,Support Vector Machine

论文评审过程：Received 4 August 2017, Revised 24 January 2018, Accepted 25 January 2018, Available online 31 January 2018, Version of Record 3 February 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.038