Fast multi-language LSTM-based online handwriting recognition
Fast multi-language LSTM-based online handwriting recognition
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DOI：
10.1007/s10032-020-00350-4
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发表时间：
2020-02-08
影响因子：
2.3
通讯作者：
Gervais, Philippe
中科院分区：
文献类型：
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作者：
Carbune, Victor;Gonnet, Pedro;Gervais, Philippe
我们描述了一个在线手写系统，该系统能够使用深度神经网络架构支持102种语言。这个新系统已经完全取代了我们以前的基于分段和解码的系统，并将大多数语言的错误率降低了20-40%。此外，我们报告了IAM-OnDB在开放和封闭数据集设置上的最新结果。该系统将序列识别方法与使用Bezier曲线的新输入编码相结合。与我们以前的系统相比，这导致识别时间快10倍。通过一系列的实验，我们确定了我们模型的最佳配置，并在一些额外的公共数据集上报告了我们的设置结果。
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 Bezier 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.