Improving handwriting based gender classification using ensemble classifiers
作者：
Highlights：
• A system to predict gender from images of handwriting using textural descriptors.
• Multiple classifiers to discriminate male and female writings.
• Classifiers combined using bagging, voting and stacking techniques.
• Generic and script-independent approach applied to English and Arabic handwritings.
• Improved results on the QUWI database once compared to state-of-the-art methods.
摘要
•A system to predict gender from images of handwriting using textural descriptors.•Multiple classifiers to discriminate male and female writings.•Classifiers combined using bagging, voting and stacking techniques.•Generic and script-independent approach applied to English and Arabic handwritings.•Improved results on the QUWI database once compared to state-of-the-art methods.
论文关键词：Handwritten documents,Gender classification,Classifier combination,Textural features
论文评审过程：Received 30 July 2016, Revised 8 April 2017, Accepted 13 May 2017, Available online 15 May 2017, Version of Record 22 May 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.05.033
