  Performances of Handwritten Text Recognition (HTR) models are largely
determined by the availability of labeled and representative training samples.
However, in many application scenarios labeled samples are scarce or costly to
obtain. In this work, we propose a self-training approach to train a HTR model
solely on synthetic samples and unlabeled data. The proposed training scheme
uses an initial model trained on synthetic data to make predictions for the
unlabeled target dataset. Starting from this initial model with rather poor
performance, we show that a considerable adaptation is possible by training
against the predicted pseudo-labels. Moreover, the investigated self-training
strategy does not require any manually annotated training samples. We evaluate
the proposed method on four widely used benchmark datasets and show its
effectiveness on closing the gap to a model trained in a fully-supervised
manner.
