StackMix and Blot Augmentations for Handwritten Text Recognition

• URL: http://arxiv.org/abs/2108.11667v1
• Date: Thu, 26 Aug 2021 09:28:22 GMT
• Title: StackMix and Blot Augmentations for Handwritten Text Recognition
• Authors: Alex Shonenkov and Denis Karachev and Maxim Novopoltsev and Mark Potanin and Denis Dimitrov
• Abstract summary: The paper describes the architecture of the neural net-work and two ways of increasing the volume of train-ing data. StackMix can also be applied to the standalone task of gen-erating handwritten text based on printed text.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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