Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwriting Recognition. (arXiv:2107.10064v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2107.10064

Handwritten text recognition in low resource scenarios, such as manuscripts
with rare alphabets, is a challenging problem. The main difficulty comes from
the very few annotated data and the limited linguistic information (e.g.
dictionaries and language models). Thus, we propose a few-shot learning-based
handwriting recognition approach that significantly reduces the human labor
annotation process, requiring only few images of each alphabet symbol. The
method consists in detecting all the symbols of a given alphabet in a textline
image and decoding …

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