License plate recognition application using extreme learning machines

2014 Third ICT International Student Project Conference (ICT-ISPC) Pub Date : 2014-03-26 DOI:10.1109/ICT-ISPC.2014.6923228

Sumanta Subhadhira, Usarat Juithonglang, Paweena Sakulkoo, Punyaphol Horata

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引用次数: 14

Abstract

Recording a car license plate is an important task for police officers or security officers to check the car of interest. However, manually recording these plates comes with problems. It is easy to make a mistake, or it can be lost. The Extreme Learning Machine (ELM) can classify the plates faster and it is a more accurate system. Therefore, this paper proposes a new license plate recognition system using ELM. The proposed system is composed of two parts: the first is a mobile application to take a picture of the car license plate, and the second is the recognition system using ELM. The recognition system entails two parts: the first is to preprocess and extract features using the histogram of oriented gradients (HOG). The second part is to classify each number and each of the Thai alphabet letters that appear on the car license plates. Also, the system will classify provinces of each plate. The results of the experiment show that the testing recognition rate when trained with 200 hidden nodes is 89.05% while the rate of correctly recognized plates is 252 out of 283 plates.

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车牌识别应用程序使用极限学习机

记录汽车牌照是警察或保安人员检查感兴趣的汽车的一项重要任务。然而，手动记录这些印版会带来问题。犯错误是很容易的，否则就会丢失。极限学习机(ELM)可以更快地对板材进行分类，这是一个更准确的系统。因此，本文提出了一种新的基于ELM的车牌识别系统。本文提出的系统由两部分组成:第一部分是对车牌进行拍照的移动应用程序，第二部分是使用ELM的识别系统。该识别系统包括两个部分:第一部分是利用定向梯度直方图(HOG)对特征进行预处理和提取。第二部分是对车牌上出现的每个数字和每个泰国字母进行分类。此外，该系统还将对每个车牌的省份进行分类。实验结果表明，使用200个隐藏节点训练时，测试识别率为89.05%，283张图像中正确识别252张。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2014 Third ICT International Student Project Conference (ICT-ISPC)

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