Recognition of CAPTCHA Characters by Supervised Machine Learning Algorithms

被引：17

作者：

Bostik, Ondrej ^{[1
]}

Klecka, Jan ^{[1
]}

机构：

[1] Brno Univ Technol, Dept Control & Instrumentat, Brno, Czech Republic

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 06期

关键词：

CAPTCHA; OCR; Supervised Learning; Template Matching; Decision Trees; k-NN; SVM; Neural Network;

D O I：

10.1016/j.ifacol.2018.07.155

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The focus of this paper is to compare several common machine learning classification algorithms for Optical Character Recognition of CAPTCHA codes. The main part of a research focuses on the comparative study of Neural Networks, k-Nearest Neighbour, Support Vector Machines and Decision Trees implemented in MATLAB Computing environment. Achieved success rates of all analyzed algorithms overcome 89%. The main difference in results of used algorithms is within the learning times. Based on the data found, it is possible to choose the right algorithm for the particular task. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：208 / 213

页数：6

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