A MSD-HMM Approach to Pen Trajectory Modeling for Online Handwriting Recognition
A MSD-HMM Approach to Pen Trajectory Modeling for Online Handwriting Recognition
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用于在线手写识别的笔轨迹建模的 MSD-HMM 方法
DOI：
10.1109/icdar.2007.20
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Yi
中科院分区：
文献类型：
--
作者：
Lei Ma;F. Soong;Peng Liu;Yi
在在线手写字符建模中，通过将相邻的真实笔画连接在一起形成连续的轨迹，可以方便地生成虚构笔画。然而，这种方法在具有相似但实际上不同轨迹的角色之间造成了混淆。在本文中，我们提出了使用多空间概率分布(MSD)来对虚构笔画和真实笔画进行联合建模。在MSD中，实笔画和虚笔画成为来自不同概率空间的观测值，并被随机建模。此外，MSD可以灵活地为每个单独的空间分配不同的特征尺寸，这使得我们可以忽略建模中可能导致奇异性问题的某些特征。在手写体汉字识别中的实验结果表明，在不同的识别系统中，MSD的字符识别准确率提高了1.3%-2.8%，其中MSD显着改善了具有相似轨迹的易混淆字符的识别率。
In modeling online handwritten characters, imaginary strokes have been conveniently generated by connecting adjacent real strokes together to form a continuous trajectory. However, this approach causes confusions among characters with similar but actually different trajectories. In this paper, we propose to use multi-space probability distribution (MSD) to model imaginary strokes jointly with real strokes. With the proposed MSD, real and imaginary strokes become observations from different probability spaces and they are modeled stochastically. Also, the flexibility in MSD to assign different feature dimensions to each individual space enables us to ignore certain features that can cause singularity problem in modeling. Experimental results obtained in handwritten Chinese character recognition indicate MSD provides 1.3%-2.8% character recognition accuracy improvement across different recognition systems where MSD significantly improves discrimination among confusable characters with similar trajectories.