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Fast multi-language LSTM-based online handwriting recognition
International Journal on Document Analysis and Recognition ( IF 2.3 ) Pub Date : 2020-02-08 , DOI: 10.1007/s10032-020-00350-4
Victor Carbune , Pedro Gonnet , Thomas Deselaers , Henry A. Rowley , Alexander Daryin , Marcos Calvo , Li-Lun Wang , Daniel Keysers , Sandro Feuz , Philippe Gervais

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 Bézier curves. This leads to up to \(10\times \) 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.

中文翻译:

快速的基于LSTM的多语言在线手写识别

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体系结构支持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,并且相对于大多数语言而言,将错误率降低了20-40%。此外,我们在IAM-OnDB上针对打开和关闭的数据集设置报告了最新的最新结果。该系统将来自序列识别的方法与使用贝塞尔曲线的新输入编码相结合。与我们以前的系统相比,这最多可导致\(10 \ times \)更快的识别时间。通过一系列实验,我们确定了模型的最佳配置,并在许多其他公共数据集上报告了设置结果。
更新日期:2020-02-08
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