  We introduce two data augmentation techniques, which, used with a
Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate (WER) and
Character Error Rate (CER) beyond best-reported results on handwriting text
recognition (HTR) tasks. We apply a novel augmentation that simulates
strikethrough text (HandWritten Blots) and a handwritten text generation method
based on printed text (StackMix), which proved to be very effective in HTR
tasks. StackMix uses weakly-supervised framework to get character boundaries.
Because these data augmentation techniques are independent of the network used,
they could also be applied to enhance the performance of other networks and
approaches to HTR. Extensive experiments on ten handwritten text datasets show
that HandWritten Blots augmentation and StackMix significantly improve the
quality of HTR models
