StackMix and Blot Augmentations for Handwritten Text Recognition
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 44 references that are in the map. First posted on arXiv 26 Aug 2021.
Builds on
16 of 44 references.
References · 16
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- 2020Attention-based Fully Gated CNN-BGRU for Russian Handwritten Textcited by 17
- 2021Mixup Without Hesitationcited by 13
4 more
- 2018Handwriting Recognition of Historical Documents with few labeled datacited by 10
- 2018Are 2D-LSTM really dead for offline text recognition?cited by 7
- 2019No Padding Please: Efficient Neural Handwriting Recognitioncited by 5
- 2017Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labellingcited by 4
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1.
2 papers.