  This paper addresses text recognition for domains with limited manual
annotations by a simple self-training strategy. Our approach should reduce
human annotation effort when target domain data is plentiful, such as when
transcribing a collection of single person's correspondence or a large
manuscript. We propose to train a seed system on large scale data from related
domains mixed with available annotated data from the target domain. The seed
system transcribes the unannotated data from the target domain which is then
used to train a better system. We study several confidence measures and
eventually decide to use the posterior probability of a transcription for data
selection. Additionally, we propose to augment the data using an aggressive
masking scheme. By self-training, we achieve up to 55 % reduction in character
error rate for handwritten data and up to 38 % on printed data. The masking
augmentation itself reduces the error rate by about 10 % and its effect is
better pronounced in case of difficult handwritten data.
