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Online handwritten devanagari stroke recognition using extended directional features

机译:使用扩展方向功能的在线手写devanagari笔画识别

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This paper describes a new feature set, called the extended directional features (EDF) for use in the recognition of online handwritten strokes. We use EDF specifically to recognize strokes that form a basis for producing Devanagari script, which is the most widely used Indian language script. It should be noted that stroke recognition in handwritten script is equivalent to phoneme recognition in speech signals and is generally very poor and of the order of 20% for singing voice. Experiments are conducted for the automatic recognition of isolated handwritten strokes. Initially we describe the proposed feature set, namely EDF and then show how this feature can be effectively utilized for writer independent script recognition through stroke recognition. Experimental results show that the extended directional feature set performs well with about 65+% stroke level recognition accuracy for writer independent data set.
机译:本文介绍了一种新功能集,称为扩展方向特征(EDF),用于识别在线手写笔划。我们专门使用EDF来识别笔画,这些笔画构成了制作梵文脚本的基础,梵文脚本是使用最广泛的印度语脚本。应当注意,手写脚本中的笔画识别等效于语音信号中的音素识别,并且通常非常差,并且对于歌唱语音而言大约为20%。为自动识别孤立的手写笔划进行了实验。最初,我们描述了提议的功能集,即EDF,然后展示了如何通过笔画识别将该功能有效地用于独立于作者的脚本识别。实验结果表明,对于独立于作者的数据集,扩展的方向性特征集具有约65 +%的笔划级别识别精度,性能良好。

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