| 基于小波变换和梯度方向的脱机手写藏文字符特征提取方法(英文) |
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| 引用本文： | 黄鹤鸣,达飞鹏,韩晓旭.基于小波变换和梯度方向的脱机手写藏文字符特征提取方法(英文)[J].东南大学学报,2014(1):27-31. |
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| 作者姓名： | 黄鹤鸣 达飞鹏 韩晓旭 |
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| 作者单位： | [1] 东南大学自动化学院,南京 210096; 青海师范大学计算机学院,西宁 810008 [2] 东南大学自动化学院,南京210096 [3] 福坦莫大学计算机与信息科学系,纽约 10458 |
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| 基金项目： | The National Natural Science Foundation of China （No. 60963016）, the National Social Science Foundation of China （No. 17BXW037）. |
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| 摘 要： | 为了提高脱机手写藏文字符的识别效果,提出了一种在小波变换基础上计算局部梯度方向直方图的特征提取方法.首先,对一个脱机手写藏文字符样本图像进行一次Haar小波变换,得到相应的一级近似分量;然后,将这个一级近似分量划分成几个等尺寸的子区域;最后,计算每个等尺寸子区域的局部梯度方向直方图,并将所有子区域的全部局部梯度方向直方图的值作为该字符图片的特征.在最近建立的脱机手写藏文字符样本数据库(THCDB)上的实验结果表明:提出的特征提取方法识别效率较高,且识别效果较好;和细节分量相比,近似分量对提高识别精度具有更大的贡献.
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| 关 键 词： | 模式识别 小波变换 梯度方向 藏文 手写字符 |
Wavelet transform and gradient direction based feature extraction method for off-line handwritten Tibetan letter recognition |
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| Huang Heming,Da Feipeng,Han Xiaoxu.Wavelet transform and gradient direction based feature extraction method for off-line handwritten Tibetan letter recognition[J].Journal of Southeast University(English Edition),2014(1):27-31. |
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| Authors: | Huang Heming Da Feipeng Han Xiaoxu |
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| Institution: | Huang Heming, Da Feipeng, Han Xiaoxu |
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| Abstract: | To improve the recognition accuracy of off-line handwritten Tibetan characters the local gradient direction histograms based on the wavelet transform are proposed as the recognition features.First for a Tibetan character sample image the first level approximation component of the Haar wavelet transform is calculated.Secondly the approximation component is partitioned into several equal-sized zones. Finally the gradient direction histograms of each zone are calculated and the local direction histograms of the approximation component are considered as the features of the character sample image.The proposed method is tested on the recently developed off-line Tibetan handwritten character sample database.The experimental results demonstrate the effectiveness and efficiency of the proposed feature extraction method.Furthermore compared with the detail components the approximation component contributes more to the recognition accuracy. |
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| Keywords: | pattern recognition wavelet transform gradient direction Tibetan handwritten character |
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