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HMM based online hand-drawn graphic symbol recognition

机译:基于HMM的在线手绘图形符号识别

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In this paper, a online hand-drawn graphic symbol recognition algorithm based on hidden Markov model is presented. A rearrangement strategy is applied to the hand-drawn symbol points in order to alleviate the influence of the difference in drawing sequence. Based on rearranged drawing points, global distance measure and local angle feature are extracted as the feature vector. After the quantization, a discrete HMM is used as the core recognizer. The experiment shows the recognition rate of our system can be above 85%.
机译:本文介绍了一种基于隐马尔可夫模型的在线手绘图形符号识别算法。重新排列策略应用于手绘符号点,以减轻绘制序列差异的影响。基于重新排列的绘图点,作为特征向量提取全局距离测量和局部角度特征。在量化之后,将离散的HMM用作核心识别器。实验表明,我们的系统的识别率可以高于85%。

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