Conventional approaches used supervised learning to estimate off-line writer identifications. In this study, we improved the off-line writer identifica- tions by semi-supervised feature learning pipeline, which trained the extra unla- beled data and the original labeled data simultaneously. In specific, we proposed a weighted label smoothing regularization (WLSR) method, which assigned the weighted uniform label distribution to the extra unlabeled data. We regularized the convolutional neural network (CNN) baseline, which allows learning more discriminative features to represent the properties of different writing styles. Based on experiments on ICDAR2013, CVL and IAM benchmark datasets, our results showed that semi-supervised feature learning improved the baseline meas- urement and achieved better performance compared with existing writer identifications approaches.

本研究通过半监督特征学习管道建立起加权标签平滑正则化（WLSR）方法，将额外的无标签数据分配给加权一致的标签分布，用以解决线下手写体识别中监督学习方法所需的训练数据和潜在的过度拟合误差问题。在ICDAR2013和CVL等知名基准数据集上，实验结果表明，该方法显著提高了基线度量，并具有现有线下写字的识别方法相竞争的表现。这些发现为离线写字体识别提供了新的视角。

半监督特征学习在作者识别中的应用