The advent of recurrent neural networks for handwriting recognition marked an
important milestone reaching impressive recognition accuracies despite the
great variability that we observe across different writing styles. Sequential
architectures are a perfect fit to model text lines, not only because of the
inherent temporal aspect of text, but also to learn probability distributions
over sequences of characters and words. However, using such recurrent paradigms
comes at a cost at training stage, since their sequential pipelines prevent
parallelization. In this work, we introduce a non-recurrent approach to
recognize handwritten text by the use of transformer models. We propose a novel
method that bypasses any recurrence. By using multi-head self-attention layers
both at the visual and textual stages, we are able to tackle character
recognition as well as to learn language-related dependencies of the character
sequences to be decoded. Our model is unconstrained to any predefined
vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do
not appear in the training vocabulary. We significantly advance over prior art
and demonstrate that satisfactory recognition accuracies are yielded even in
few-shot learning scenarios.

使用变形金刚模型的多头自我注意力层，无需回归方法且具有超出预定义词汇表的单词识别能力，能实现很高的手写识别精度，即使在少量样本学习情况下也可取得满意的结果。