Handwriting is one of the most frequently occurring patterns in everyday life and with it comes challenging applications such as handwriting recognition, writer identification and signature verification. In contrast to offline HWR that only uses spatial information (i.e., images), online HWR uses richer spatio-temporal information (i.e., trajectory data or inertial data). While there exist many offline HWR datasets, there are only little data available for the development of OnHWR methods on paper as it requires hardware-integrated pens. This paper presents data and benchmark models for real-time sequence-to-sequence learning and single character-based recognition. Our data are recorded by a sensor-enhanced ballpoint pen, yielding sensor data streams from triaxial accelerometers, a gyroscope, a magnetometer and a force sensor at 100 Hz. We propose a variety of datasets including equations and words for both the writer-dependent and writer-independent tasks. Our datasets allow a comparison between classical OnHWR on tablets and on paper with sensor-enhanced pens. We provide an evaluation benchmark for seq2seq and single character-based HWR using recurrent and temporal convolutional networks and transformers combined with a connectionist temporal classification (CTC) loss and cross-entropy (CE) losses. Our convolutional network combined with BiLSTMs outperforms transformer-based architectures, is on par with InceptionTime for sequence-based classification tasks and yields better results compared to 28 state-of-the-art techniques. Time-series augmentation methods improve the sequence-based task, and we show that CE variants can improve the single classification task. Our implementations together with the large benchmark of state-of-the-art techniques of novel OnHWR datasets serve as a baseline for future research in the area of OnHWR on paper.

手写是日常生活中最常见的模式之一，随之而来的是具有挑战性的应用，例如手写识别、作家识别和签名验证。与仅使用空间信息（即图像）的离线HWR相比，在线HWR使用更丰富的时空信息（即轨迹数据或惯性数据）。虽然存在许多离线 HWR 数据集，但用于开发 OnHWR 方法在纸上的可用数据很少，因为它需要硬件集成的笔。本文介绍了实时序列到序列学习和基于单个字符的识别的数据和基准模型。我们的数据由传感器增强型圆珠笔记录，产生来自三轴加速度计、陀螺仪、磁力计和力传感器的 100 Hz 传感器数据流。我们为依赖于作者和独立于作者的任务提出了各种数据集，包括方程式和单词。我们的数据集允许在平板电脑上的经典 OnHWR 和带有传感器增强笔的纸上进行比较。我们为 seq2seq 和基于单字符的 HWR 提供评估基准，使用循环和时间卷积网络和转换器，结合连接主义时间分类 (CTC) 损失和交叉熵 (CE) 损失。我们的卷积网络与 BiLSTM 相结合，优于基于转换器的架构，在基于序列的分类任务中与 InceptionTime 相当，并且与 28 种最先进的技术相比，产生了更好的结果。时间序列增强方法改进了基于序列的任务，我们表明 CE 变体可以改进单一分类任务。