In this study, a functional-link-based neural network (FLNN) is adopted to develop an optical character recognition (OCR) system. The proposed OCR system contains the feature extraction and the FLNN classifier. The OCR system is valid for recognizing the capital letters and Arabic numerals. The proposed feature extraction technique includes the distance between each black pixel and concentric circles feature. They have the properties of rotation and noise invariant. In addition, the proposed FLNN with the expanded functional-basis has the properties of anti-noise and distortion characteristics. Finally, some experimental results are introduced to illustrate the performance and effectiveness of the proposed feature extraction and FLNN classifier. The proposed system performs 100% accuracy for actual characters and at least 94% even the characters has variations of location, size, rotation, and noisy. For different fonts (Arial and Time New Roman), the proposed system almost perform well and has 80% accuracy.
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机译：在这项研究中，基于功能链接的神经网络（FLNN）被用来开发光学字符识别（OCR）系统。提出的OCR系统包含特征提取和FLNN分类器。 OCR系统可有效识别大写字母和阿拉伯数字。提出的特征提取技术包括每个黑色像素与同心圆特征之间的距离。它们具有旋转和噪声不变的特性。另外，所提出的具有扩展的功能基础的FLNN具有抗噪声和失真特性的特性。最后，引入一些实验结果来说明所提出的特征提取和FLNN分类器的性能和有效性。所提出的系统对实际字符执行100％的精度，并且即使字符具有位置，大小，旋转和噪声的变化，也至少要达到94％的精度。对于不同的字体（Arial和Time New Roman），建议的系统几乎可以很好地执行并且具有80％的精度。
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