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StackMix and Blot Augmentations for Handwritten Text Recognition

2021-08-26 09:28:22
Alex Shonenkov, Denis Karachev, Maxim Novopoltsev, Mark Potanin, Denis Dimitrov
arXiv_CV
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2108.11667

PDF

https://arxiv.org/pdf/2108.11667.pdf

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