StackMix and Blot Augmentations for Handwritten Text Recognition
This work addresses the problem of improving text recognition accuracy for handwritten documents, which is incremental as it builds on existing methods with new augmentations.
The paper tackled handwritten text recognition by proposing a system that outperforms state-of-the-art methods on datasets like Bentham, IAM, and Saint Gall, achieving concrete improvements in accuracy.
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.