We describe an online handwriting system that is able to support 102
languages using a deep neural network architecture. This new system has
completely replaced our previous Segment-and-Decode-based system and reduced
the error rate by 20%-40% relative for most languages. Further, we report new
state-of-the-art results on IAM-OnDB for both the open and closed dataset
setting. The system combines methods from sequence recognition with a new input
encoding using B\'ezier curves. This leads to up to 10x faster recognition
times compared to our previous system. Through a series of experiments we
determine the optimal configuration of our models and report the results of our
setup on a number of additional public datasets.

本文介绍了一种支持 102 种语言的在线手写系统，采用深度神经网络架构和贝塞尔曲线的新输入编码，与之前的系统相比降低了 20%-40% 的错误率，并在 IAM-OnDB 数据集上取得了新的最优结果。通过实验确定了模型的最优配置，并在多个公共数据集上进行了评估。