  Data augmentation is usually used by supervised learning approaches for
offline writer identification, but such approaches require extra training data
and potentially lead to overfitting errors. In this study, a semi-supervised
feature learning pipeline was proposed to improve the performance of writer
identification by training with extra unlabeled data and the original labeled
data simultaneously. Specifically, we proposed a weighted label smoothing
regularization (WLSR) method for data augmentation, which assigned the weighted
uniform label distribution to the extra unlabeled data. The WLSR method could
regularize the convolutional neural network (CNN) baseline to allow more
discriminative features to be learned to represent the properties of different
writing styles. The experimental results on well-known benchmark datasets
(ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning
approach could significantly improve the baseline measurement and perform
competitively with existing writer identification approaches. Our findings
provide new insights into offline write identification.
