A writer adaptation method for isolated handwritten digit recognition based on Ensemble Projection of features

Hamidreza Hosseinzadeh, F. Razzazi
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引用次数: 1

Abstract

Learning handwriting categories fail to perform well when trained and tested on data from different databases. In this paper, we propose a novel framework of Ensemble Projection (EP) for writer adaptation. We employed EP as a feature transformation method which can be combined with different types of classifiers for unsupervised and semi-supervised adaptation. Experiments on a handwritten digit dataset demonstrate that EP learning can increase recognition rates significantly, both in the unsupervised and semi-supervised cases.
基于特征集成投影的孤立手写数字识别的写作者自适应方法
当对来自不同数据库的数据进行训练和测试时,学习笔迹分类的效果不佳。在本文中,我们提出了一个新的作家改编的集合投影(EP)框架。我们将EP作为一种特征转换方法,可以与不同类型的分类器结合进行无监督和半监督自适应。在手写数字数据集上的实验表明,EP学习在无监督和半监督情况下都可以显著提高识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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