使用CNN图像滤波器的多尺度手写字符识别

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) Pub Date : 2002-08-07 DOI:10.1109/IJCNN.2002.1007454

E. Saatci, V. Tavsanoglu

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引用次数: 12

摘要

本文提出了一个由三个单尺度识别系统组成的多尺度字符识别系统。该系统使用了一组由细胞神经网络(CNN)实现的gabor型滤波器。以26个测试字符作为模板的测试集和26个未知手写测试字符的4个子集组成的测试集，提供了最高96%和平均93%的正确率。与现有的单尺度识别系统相比，这是一个相当大的改进。

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Multiscale handwritten character recognition using CNN image filters

This paper presents a multi-scale character recognition system consisting of three single-scale recognition systems. The system uses a filter bank of Gabor-type filters implemented by a cellular neural network (CNN). Based on a test set of 26 test characters acting as template and a set consisting of four subsets of 26 unknown handwritten test characters, a maximum 96% and an average 93% correct recognition is provided. This is a considerable improvement over the performance of existing single-scale recognition systems.

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Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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