Boosting offline handwritten text recognition in historical documents with few labeled lines

Abstract

In this paper we address the problem of offline handwritten text recognition (HTR) in historical documents when few labeled samples are available and some of them contain errors in the train set. Our three main contributions are: first, we analyze how to perform transfer learning (TL) from a massive database to a smaller historical database, analyzing which layers of the model need fine-tuning. Second, we analyze methods to efficiently combine TL and data augmentation (DA). Finally, we propose an algorithm to mitigate the effects of incorrect labeling in the training set. The methods are analyzed over the ICFHR 2018 competition database, Washington and Parzival. Combining all these techniques, we demonstrate a remarkable reduction of CER (up to 6 percentage points in some cases) in the test set with little complexity overhead.