Knowledge based text character recognition using Fourier transform

[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence Pub Date : 1990-11-06 DOI:10.1109/TAI.1990.130401

N. Bourbakis, A. T. Gumahad

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引用次数: 3

Abstract

The Fourier transformation was applied on a set of typed text characters, extracting their unique features and developing an appropriate knowledge base for quick text character recognition. The use of this technique may also allow the development of an adaptive recognizer capable of learning through proper development of the classifier. The proposed technique computes the Fourier transform of the input string derived by the HVP (horizontal-vertical projection) process. In particular, the string created by the HVP scheme is a combination of two strings from the horizontal and vertical projections. The coefficients of the input string-derived Fourier series are compared with the features of the known characters, and classification is performed based on the closeness of the features set. Analysis of test results showed that the Fourier transform approach for feature extraction and the simple classification technique chosen in this project displayed a classification accuracy of over 80% for a limited set of conditions.<>

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基于知识的傅立叶变换文本字符识别

对输入的文本字符进行傅里叶变换，提取其独特特征，建立相应的知识库，实现文本字符的快速识别。这种技术的使用也可以通过分类器的适当发展来开发一种能够学习的自适应识别器。该技术计算由HVP(水平-垂直投影)过程导出的输入字符串的傅里叶变换。特别是，HVP方案创建的管柱是来自水平和垂直投影的两个管柱的组合。将输入字符串傅立叶级数的系数与已知字符的特征进行比较，并根据特征集的接近度进行分类。测试结果分析表明，在有限的条件下，本项目中选择的傅里叶变换特征提取方法和简单分类技术的分类准确率超过80%

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[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence

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