多级混合阿拉伯/印度数字OCR系统
Multistage Hybrid Arabic/Indian Numeral OCR System
摘要：
OCR在邮政服务中的使用尚未普及，仍有许多国家手动处理邮件分拣。一些国家的邮政服务正在使用自动阿拉伯/印度数字光学字符识别（OCR）系统，但在邮件分拣过程中仍然存在错误，从而导致效率降低。研究快速和有效的识别算法/系统的需要是重要的，以便正确地从邮件地址读取邮政编码，并消除邮件分拣阶段的任何错误。本研究的目的是从邮件地址中识别印刷体数字邮政编码。所提出的系统是由三种不同的特征提取方法组成的多级混合系统，二进制、分区和模糊特征，以及三种不同的分类器，即，汉明网络、欧几里德距离和模糊神经网络分类器。所提出的系统，系统地比较这些方法的性能，并确保数字被正确识别。综合的结果提供了一个非常高的识别率，优于其他已知的开发方法在文献中。
英文摘要：
The use of OCR in postal services is not yet universal and there are still many countries that process mail sorting manually. Automated Arabic/Indian numeral Optical Character Recognition (OCR) systems for Postal services are being used in some countries, but still there are errors during the mail sorting process, thus causing a reduction in efficiency. The need to investigate fast and efficient recognition algorithms/systems is important so as to correctly read the postal codes from mail addresses and to eliminate any errors during the mail sorting stage. The objective of this study is to recognize printed numerical postal codes from mail addresses. The proposed system is a multistage hybrid system which consists of three different feature extraction methods, i.e., binary, zoning, and fuzzy features, and three different classifiers, i.e., Hamming Nets, Euclidean Distance, and Fuzzy Neural Network Classifiers. The proposed system, systematically compares the performance of each of these methods, and ensures that the numerals are recognized correctly. Comprehensive results provide a very high recognition rate, outperforming the other known developed methods in literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
