Papers › StackMix and Blot Augmentations for Handwritten Text Recognition
StackMix and Blot Augmentations for Handwritten Text Recognition
Alex Shonenkov, Denis Karachev, Maxim Novopoltsev, Mark Potanin, Denis Dimitrov
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Handwritten Text Recognition | Bentham | StackMix+Blots | CER | 1.73 | #1 of 1 | Archive leaderboard | report |
| Handwritten Text Recognition | Digital Peter | StackMix+Blots | CER | 2.5 | #1 of 1 | Archive leaderboard | report |
| Handwritten Text Recognition | HKR | StackMix+Blots | CER | 3.49 | #1 of 1 | Archive leaderboard | report |
| Handwritten Text Recognition | IAM-B | StackMix+Blots | CER | 3.77 | #1 of 1 | Archive leaderboard | report |
| Handwritten Text Recognition | IAM-D | StackMix+Blots | CER | 3.01 | #1 of 1 | Archive leaderboard | report |
| Handwritten Text Recognition | Saint Gall | StackMix+Blots | CER | 3.65 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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