Practical Recognition System for Text Printed on Clear Reflected Material

Author:

Mohammad Khader1,Agaian Sos2

Affiliation:

1. Ingram School of Engineering, Texas State University-San Marcos, 601 University Drive, San Marcos, TX 78666-4684, USA

2. Electrical and Computer Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA

Abstract

Text embedded in an image contains useful information for applications in the medical, industrial, commercial, and research fields. While many systems have been designed to correctly identify text in images, no work addressing the recognition of degraded text on clear plastic has been found. This paper posits novel methods and an apparatus for extracting text from an image with the practical assumption: (a) poor background contrast, (b) white, curved, and/or differing fonts or character width between sets of images, (c) dotted text printed on curved reflective material, and/or (d) touching characters. Methods were evaluated using a total of 100 unique test images containing a variety of texts captured from water bottles. These tests averaged a processing time of ~10 seconds (using MATLAB R2008A on an HP 8510 W with 4 G of RAM and 2.3 GHz of processor speed), and experimental results yielded an average recognition rate of 90 to 93% using customized systems generated by the proposed development.

Publisher

Hindawi Limited

Subject

Management Science and Operations Research,Mechanical Engineering,Energy Engineering and Power Technology

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automated Line Segmentation for Gurmukhi Text Recognition System;2021 2nd International Conference on Computational Methods in Science & Technology (ICCMST);2021-12

2. An adaptive text-line extraction algorithm for printed Arabic documents with diacritics;Multimedia Tools and Applications;2020-09-11

3. Performance Improvement of Dot-Matrix Character Recognition by Variation Model Based Learning;Computer Vision - ACCV 2014 Workshops;2015

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