This paper proposes a handwritten text recognition(HTR) system that outperforms current state-of-the-artmethods. The comparison was carried out on three of themost frequently used in HTR task datasets, namely Ben-tham, IAM, and Saint Gall. In addition, the results on tworecently presented datasets, Peter the Greats manuscriptsand HKR Dataset, are provided.The paper describes the architecture of the neural net-work and two ways of increasing the volume of train-ing data: augmentation that simulates strikethrough text(HandWritten Blots) and a new text generation method(StackMix), which proved to be very effective in HTR tasks.StackMix can also be applied to the standalone task of gen-erating handwritten text based on printed text.
@article{arxiv.2108.11667,
title = {StackMix and Blot Augmentations for Handwritten Text Recognition},
author = {Alex Shonenkov and Denis Karachev and Maxim Novopoltsev and Mark Potanin and Denis Dimitrov},
journal= {arXiv preprint arXiv:2108.11667},
year = {2021}
}