Handwritten character recognition using wavelet energy and extreme learning machine
Handwritten character recognition using wavelet energy and extreme learning machine
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DOI：
10.1007/s13042-011-0049-5
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发表时间：
2011-09
影响因子：
5.6
通讯作者：
Binu P. Chacko;V. Krishnan;G. Raju;P. B. Anto
中科院分区：
文献类型：
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作者：
Binu P. Chacko;V. Krishnan;G. Raju;P. B. Anto
研究了基于小波能量特征和极限学习机的手写体马拉雅拉姆文字符识别。小波能量是一种新的鲁棒性参数，它是利用小波变换得到的。它可以减少不同类型的噪声在不同级别的影响。WEF可以反映不同尺度下多个方向上的特征的WE分布。对于非振荡模式，小波系数的幅值随小波分解尺度的增大而增大。不同分解层次的小波对字符图像的分辨能力不同。这些特征构成了用于分类的手写字符模式。不同分类器的传统学习算法远比要求的慢。因此，我们使用了一个非常快速的学习算法，称为ELM单隐层前馈网络（SLFN），它随机选择的输入权重和解析确定SLFN的输出权重。该算法的学习速度比传统的流行的前馈神经网络学习算法快得多。这种特征向量，分类器组合给出了良好的识别精度在第6级的小波分解。
This paper deals with the recognition of handwritten Malayalam character using wavelet energy feature (WEF) and extreme learning machine (ELM). The wavelet energy (WE) is a new and robust parameter, and is derived using wavelet transform. It can reduce the influences of different types of noise at different levels. WEF can reflect the WE distribution of characters in several directions at different scales. To a non oscillating pattern, the amplitudes of wavelet coefficients increase when the scale of wavelet decomposition increase. WE of different decomposition levels have different powers to discriminate the character images. These features constitute patterns of handwritten characters for classification. The traditional learning algorithms of the different classifiers are far slower than required. So we have used an extremely fast leaning algorithm called ELM for single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN. This algorithm learns much faster than traditional popular learning algorithms for feed forward neural networks. This feature vector, classifier combination gave good recognition accuracy at level 6 of the wavelet decomposition.