脊波变换对手写体识别的影响——基于超大型卡纳达文数据集的研究

C. Naveena, Manjunath Aradhya
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引用次数: 7

摘要

手写体字体的变化很大,字体的大小和方向角度也各不相同,因此手写体字符识别是一个难题。本文提出了一种基于脊波变换的无约束手写体卡纳达语字符识别方法。脊小波是捕捉和表示二维空间中的一维奇点的有力工具[7]。利用脊波变换提取特征图像的低通能量,并将其送入主成分分析进行特征提取。我们在非常大的卡纳达手写体数据库上进行了实验。班级规模为200人,取得了令人鼓舞的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An impact of ridgelet transform in handwritten recognition: A study on very large dataset of Kannada script
Handwritten character recognition is a difficult problem due to the great variations on writing styles, different size and orientation angle of the characters. In this paper, we propose an unconstrained handwritten Kannada character recognition based on the ridgelet transforms. Ridglets are a powerful instrument in catching and representing mono-dimensional singularities in bi dimensional space [7]. Ridgelet transforms is used to extracts low pass energy of character image and is then fed to PCA for feature extraction. We conducted experiment on very large database of handwritten Kannada character. The size of the class was 200 and encouraging results are obtained.
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