Delayed strokes, such as i-dots and t-crosses, cause a challenge in online handwriting recognition by introducing an extra source of variation in the sequence order of the handwritten input. The problem is especially relevant for languages where delayed strokes are abundant and training data are limited. Studies for handling delayed strokes have mainly focused on Arabic and Farsi scripts where the problem is most severe, with less attention devoted for scripts based on the Latin alphabet. This study aims to investigate the effectiveness of the delayed stroke handling methods proposed in the literature. Evaluated methods include the removal of delayed strokes and embedding delayed strokes in the correct writing order, together with their variations. Starting with new definitions of a delayed stroke, we tested each method using both hidden Markov model classifiers separately for English and Turkish and bidirectional long short-term memory networks for English. For both the UNIPEN and Turkish datasets, the best results are obtained with hidden Markov model recognizers by removing all delayed strokes, with up to 2.13% and 2.03% points accuracy increases over the respective baselines. In case of the bidirectional long short-term memory networks, stroke order correction of the delayed strokes by embedding performs the best, with 1.81% (raw) and 1.72% (post-processed) points improvements above the baseline.

中文翻译：

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在线手写中延迟笔触处理方法的比较研究

延迟的笔触（例如i点和t叉）通过在手写输入的顺序顺序中引入额外的变化来源，对在线手写识别提出了挑战。对于延迟笔迹丰富且训练数据有限的语言，此问题尤其重要。关于处理延迟笔画的研究主要集中在阿拉伯和波斯文的问题最为严重的地方，而对基于拉丁字母的文字的关注则较少。这项研究旨在调查文献中提出的延迟卒中处理方法的有效性。评估的方法包括删除延迟的笔触，并以正确的书写顺序嵌入延迟的笔触及其变体。从延迟卒中的新定义开始，我们分别针对英语和土耳其语使用两个隐藏的马尔可夫模型分类器以及针对英语的双向长短期记忆网络对每种方法进行了测试。对于UNIPEN和土耳其数据集，通过删除所有延迟笔画，使用隐藏的马尔可夫模型识别器可获得最佳结果，在各自的基线上，精度分别提高了2.13％和2.03％。在双向长短期记忆网络的情况下，通过嵌入对延迟的笔划进行笔划顺序校正的效果最佳，与基线相比，点改进了1.81％（原始）和1.72％（后处理）。隐藏的马尔可夫模型识别器可通过消除所有延迟的笔划来获得最佳结果，在各自的基线上，精度分别提高了2.13％和2.03％。在双向长短期记忆网络的情况下，通过嵌入对延迟的笔划进行笔划顺序校正的效果最佳，与基线相比，点改进了1.81％（原始）和1.72％（后处理）。隐藏的马尔可夫模型识别器可通过消除所有延迟的笔划来获得最佳结果，在各自的基线上，精度分别提高了2.13％和2.03％。在双向长短期记忆网络的情况下，通过嵌入对延迟的笔划进行笔划顺序校正的效果最佳，与基线相比，点改进了1.81％（原始）和1.72％（后处理）。