Data augmentation is typically used by supervised feature learning approaches for offline writer identification, but such approaches require a mass of additional training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline is proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we propose a weighted label smoothing regularization (WLSR) method for data augmentation, which assigns a weighted uniform label distribution to the extra unlabeled data. The WLSR method regularizes 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 significantly improves the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline writer identification.
@cite_41: Abstract This paper describes a method for robust offline writer identification. We propose to use RootSIFT descriptors computed densely at the script contours. GMM supervectors are used as encoding method to describe the characteristic handwriting of an individual scribe. GMM supervectors are created by adapting a background model to the distribution of local feature descriptors. Finally, we propose to use Exemplar-SVMs to train a document-specific similarity measure. We evaluate the method on three publicly available datasets (ICDAR CVL KHATT) and show that our method sets new performance standards on all three datasets. Additionally, we compare different feature sampling strategies as well as other encoding methods.
@cite_10: In this paper we describe a database that consists of handwritten English sentences. It is based on the Lancaster-Oslo Bergen (LOB) corpus. This corpus is a collection of texts that comprise about one million word instances. The database includes 1,066 forms produced by approximately 400 different writers. A total of 82,227 word instances out of a vocabulary of 10,841 words occur in the collection. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. However, it is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used, because this knowledge can be automatically derived from the underlying corpus. The database also includes a few image-processing procedures for extracting the handwritten text from the forms and the segmentation of the text into lines and words.
@cite_25: 
In @cite_25 , Christlein created a combined dataset (MERGED) consisting of 559 scribes with four documents per writer, resulting in 2236 documents from the ICDAR2013 and CVL datasets. Thereby, the training set was enlarged, and the outcomes on the MERGED datasets slightly differ from the image vocabularies that can be calculated from the ICDAR2013 experimental set or the CVL dataset. Furthermore, @cite_41 showed that the identification rate on the CVL test set could be improved by adding additional datasets (ICDAR2011 and IAM @cite_10 ) into the CVL training set. Although existing data augmentation approaches have the capability to improve the identification performance using the extra data, we can imagine that it requires a large amount of extra labeled data. In practice, however, we do not have access to collect a large number of samples for writer identification.