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3D Handwriting Characters Recognition with Symbolic-Based Similarity Measure of Gyroscope Signals Embedded in Smart Phone

机译:智能手机中嵌入的基于陀螺仪信号的基于符号相似度的3D手写字符识别

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摘要

In this paper, we present a 3D handwriting character recognition algorithm based on a symbolic representation of angular velocity signal generated from Gyroscope sensor included in a smart phone. Characters are writing in a 3D air space by a user via a wearable smart phone device. The main insight behind the use of a symbolic representation is to tackle storage and time processing challenge required in such application with mobile device. That is, we introduce a similarity distance measure that combines the Symbolic Aggregate approXimation (SAX) distance with angular trend distance. The proposed similarity distance measure requires additional information about the trend of the Time Series Data (TSD) and will be used in classification with the nearest neighbor rule. In addition, we extend the distance measure to handle multidimensional data and the classification is achieved using a greedy time warping algorithm. This algorithm internally uses a dynamic programming that overcomes the quadratic time complexity of the classical dynamic time warping search. Experiments on a real database of the 26 lowercase letters in the English alphabet show that our method improves the original and two variation of the SAX similarity search algorithm.
机译:在本文中,我们基于智能手机中包含的陀螺仪传感器生成的角速度信号的符号表示,提出了一种3D手写字符识别算法。用户通过可穿戴智能电话设备在3D空间中书写角色。使用符号表示背后的主要见解是解决此类应用程序在移动设备上所需的存储和时间处理挑战。也就是说,我们引入了一种相似距离度量,该度量将符号聚合近似(SAX)距离与角度趋势距离相结合。拟议的相似性距离度量需要有关时间序列数据(TSD)趋势的其他信息,并将用于与最近邻规则进行分类。此外,我们扩展了距离度量以处理多维数据,并使用贪婪时间扭曲算法实现了分类。该算法在内部使用动态编程,可以克服经典动态时间规整搜索的二次时间复杂性。在英文字母的26个小写字母的真实数据库中进行的实验表明,我们的方法改进了SAX相似性搜索算法的原始和两个变体。

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