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Performances of Handwritten Text Recognition (HTR) models are largelydetermined by the availability of labeled and representative training samples.However, in many application scenarios labeled samples are scarce or costly toobtain. In this work, we propose a self-training approach to train a HTR modelsolely on synthetic samples and unlabeled data. The proposed training schemeuses an initial model trained on synthetic data to make predictions for theunlabeled target dataset. Starting from this initial model with rather poorperformance, we show that a considerable adaptation is possible by trainingagainst the predicted pseudo-labels. Moreover, the investigated self-trainingstrategy does not require any manually annotated training samples. We evaluatethe proposed method on four widely used benchmark datasets and show itseffectiveness on closing the gap to a model trained in a fully-supervisedmanner.

Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation