The Transformer architecture is shown to provide a powerful machine
transduction framework for online handwritten gestures corresponding to glyph
strokes of natural language sentences. The attention mechanism is successfully
used to create latent representations of an end-to-end encoder-decoder model,
solving multi-level segmentation while also learning some language features and
syntax rules. The additional use of a large decoding space with some learned
Byte-Pair-Encoding (BPE) is shown to provide robustness to ablated inputs and
syntax rules. The encoder stack was directly fed with spatio-temporal data
tokens potentially forming an infinitely large input vocabulary, an approach
that finds applications beyond that of this work. Encoder transfer learning
capabilities is also demonstrated on several languages resulting in faster
optimisation and shared parameters. A new supervised dataset of online
handwriting gestures suitable for generic handwriting recognition tasks was
used to successfully train a small transformer model to an average normalised
Levenshtein accuracy of 96% on English or German sentences and 94% in French.

本文中，使用 Transformer 模型结构对在线手写手势符号转化为自然语言句子进行了研究，并展示了该模型的编码器解决多级分割、学习某些语言特征和语法规则等方面的优秀表现。此外，使用学习的 Byte-Pair-Encoding（BPE）和大型解码空间为数据输入和语法规则提供了鲁棒性。研究表明该模型具有迁移学习能力，适用于许多其他语言，并可用于泛用性的手写识别任务。最终，基于一个新的手写手势数据集训练小型 Transformer 模型，对于英语、德语句子平均归一化 Levenshtein 准确率达到 96％，对于法语则为 94％。