Optimization of ANPR algorithm on android mobile phone

A. Mutholib, T. Gunawan, J. Chebil, M. Kartiwi
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引用次数: 6

Abstract

Since the past decades, many researchers proposed their methods to recognize the vehicle number plate. One of the methods is template matching which is executed in the optical character recognition (OCR) step of the automatic number plate recognition (ANPR) system. In previous researches, many researchers are used a high end desktop PC and high resolution camera to implement the ANPR system. In this paper, the optimization of ANPR algorithm on limited hardware of Android mobile phone is presented. First, various steps to optimize ANPR and OCR block using template matching are described. Our proposed algorithm was based on Tesseract library. For comparison purpose, the template matching based OCR will be compared to Artificial Neural Network (ANN) based OCR. The optimization on ANPR was performed as currently there is no image processing tool available on the standard Android mobile phone. By optimization of ANPR, many advantages could be achieved, such as higher recognition accuracy, less resource consumption, and less computational complexity. Results on 30 images showed that the recognition rate was 97.46% while the processing time was 1.13.
android手机上ANPR算法的优化
在过去的几十年里,许多研究者提出了自己的车牌识别方法。车牌自动识别(ANPR)系统的光学字符识别(OCR)步骤中执行模板匹配是其中的一种方法。在以往的研究中,许多研究人员都是使用高端台式PC机和高分辨率相机来实现ANPR系统。本文研究了Android手机有限硬件条件下ANPR算法的优化问题。首先,介绍了利用模板匹配优化ANPR和OCR块的各个步骤。我们提出的算法基于Tesseract库。为了比较,将基于模板匹配的OCR与基于人工神经网络(ANN)的OCR进行比较。对ANPR进行优化是因为目前标准Android手机上没有可用的图像处理工具。通过优化ANPR,可以获得更高的识别精度、更少的资源消耗和更少的计算复杂度等优点。对30幅图像的处理结果表明,该方法的识别率为97.46%,处理时间为1.13。
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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