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StackMix and Blot Augmentations for Handwritten Text Recognition

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arxiv 2108.11667 v1 pith:ZRBJXMXW submitted 2021-08-26 cs.CV

Alex Shonenkov , Denis Karachev , Maxim Novopoltsev , Mark Potanin , Denis Dimitrov This is my paper · ORCID
classification cs.CV
keywords texthandwrittenstackmixdatasetsrecognitiontaskadditionapplied
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques

    cs.CV 2025-07 reject novelty 2.0 of 10

    A systematic review of offline handwritten text recognition augmentation and generation methods, whose claimed 55-paper corpus is contradicted by its own figures and reference list.

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