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A novel multistage classification and Wavelet based kernel generation for handwritten Marathi compound character recognition

机译:一种新颖的多级分类和基于小波的核生成,用于手写Marathi复合字符识别

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This paper presents a novel approach for recognition of unconstrained handwritten Marathi compound characters. The recognition is carried out using multistage feature extraction and classification scheme. The initial stages of feature extraction are based upon the structural features and the classification of the characters is done according to their parameters. The final stage of feature extraction employs generation of kernels using Wavelet transform. A single level Wavelet decomposition is used to generate the approximation coefficients. These coefficients are stored as kernels for matching. A modified wavelet based kernel generation method is also implemented. The recognition is done by template matching in both the cases. The results are analyzed using both the kernel generation techniques for varying resize factors. The recognition rate achieved from the proposed method is 95.89% and 96.00% for 16×16 and 32×32 resize factors respectively with wavelet based kernels and 96.41% and 97.94% for 16×16 and 32×32 resize factors respectively with modified wavelet based kernels.
机译:本文提出了一种新颖的识别马拉松手写体复合字符的方法。使用多阶段特征提取和分类方案进行识别。特征提取的初始阶段基于结构特征,并且根据字符的参数对字符进行分类。特征提取的最后阶段使用小波变换生成内核。单级小波分解用于生成近似系数。这些系数存储为内核以进行匹配。还实现了一种改进的基于小波的核生成方法。在两种情况下,都通过模板匹配来完成识别。使用两种内核生成技术针对不同的调整大小因子来分析结果。提出的方法对基于小波的核的16×16和32×32调整因子的识别率分别为95.89%和96.00%,对于基于小波的核,对于16×16和32×32调整因子分别为96.41%和97.94%内核。

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