独立分量重建模型的手写数字字符识别
Handwritten Digit Character Recognition by Model Reconstruction Based on Independent Component Analysis
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摘要: 针对在手写字符识别中,因于书写习惯和风格的不同,造成字符模式不稳定的问题,利用独立分量对信号重建的良好性能,采用独立分量分析的方法抽取字符独立特征,并建立字符重建模型;通过对重建模型的误差分析进行字符识别;对美国国家邮政局USPS(US Postal Service database)字库中全部数字字符完整的识别实验,证实了算法的鲁棒性和准确性.Abstract: To overcome handwritten character pattern's instability caused by different writing styles, a novel approach is proposed in this paper. By the approach, firstly, Independent Component Analysis (ICA) is used to extract character features and reconstruct character models. And character recognition is then conducted based on the error analysis of reconstructed models'. The proposed algorithm is tested on the entire USPS character database, and the experimental results validate the robustness and accuracy of the proposed algorithm.
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