i-manager Publications

Denoising QR Code for Recognition of Mobile Camera Captured Images

S.Haripriya*, T.Srisuma**, Y.Anilkumar***, T.Manojkumar****, Shaik Azeez*****

*-*****Department of Electonics and Communication, Lendi Institute of Engineering and Technology (Affiliated by JNTUK) Vizinagaram, Andhra Pradesh, India.

Periodicity:July - December'2021
DOI : https://doi.org/10.26634/jpr.8.2.16540

Abstract

QR code verification is commonly used in many business applications. Every image which comes into scanner for classification, contains data about the item to which it is tagged. Standardized QR code symbols can store more data both vertically and horizontally. All the QR codes appear to be comparable in appearance, while all encoded code has different data. The accessibility of phones with computerized camera gives users a portable platform for unleashing the standard tag, rather than using a specialized scanner, as the conventional scanners have several limitations. The purpose of this article is to remove noise added to the QR code images captured by mobile phones using image processing techniques. After removing the noise the image can be used for recognizing the data associated with the QR code. The proposed methodology was tested in MATLAB and the results are presented.

Keywords

QR Code, Camera Phone, Digital Noise, Denoising, Image Processing.

How to Cite this Article?

Haripriya, S., Srisuma, T., Anilkumar, Y., Manojkumar, T., and Azeez, S. (2021). Denoising QR Code for Recognition of Mobile Camera Captured Images. i-manager's Journal on Pattern Recognition, 8(2), 26-30. https://doi.org/10.26634/jpr.8.2.16540

References

[1]. Al-Amri, S. S., Kalyankar, N. V., & Khamitkar, S. D. (2010). Image segmentation by using edge detection. International Journal on Computer Science and Engineering, 2(3), 804-807.

[2]. Chen, C. W. (2010). Impact of quality antecedents on taxpayer satisfaction with online tax-filing systems—An empirical study. Information & Management, 47(5-6), 308-315.

[3]. Dieckhaus, C. (n.d.). QR Codes. Kaggle.

[4]. Dharampal, & Mutneja, V. (2015). Methods of image edge detection: A review. Journal of Electrical & Electronic Systems, 4(2), 1000150

[5]. Gaur, P., & Tiwari, S. (2014). Recognition of 2D barcode images using edge detection and morphological operation. International Journal of Computer Science and Mobile Computing, 3(4), 1277-1282.

[6]. Gonzalez, R. C, & Woods, R. E (2008). Digital image processing. Pearson Education.

[7]. IDAutomation.com. (n.d.). What is the difference between 1D (Linear) and 2D barcodes?

[8]. Kalpana, M., Kishorebabu, G., & Sujatha, K. (2012). Extraction of edge detection using digital image processing techniques. International Journal of Computational Engineering Research, 2(5), 1562-1566.

[9]. Li, M., Cao, P., Feng, L., Yu, L., Chen, J., & Wang, J. (2017, June). The research of QR code image correction based on image gray feature. In 2017, First International Conference on Electronics Instrumentation & Information Systems (EIIS) (pp. 1-5). IEEE.

[10]. Lin, J., & Fuh, C. (2013). 2D barcode image decoding. Mathematical Problems in Engineering, 2013, 1-10.

[11]. Maini, R., & Aggarwal, H. (2009). Study and comparison of various image edge detection techniques. International Journal of Image Processing (IJIP), 3(1), 1-11.

[12]. Shrivakshan, G. T., & Chandrasekar, C. (2012). A comparison of various edge detection techniques used in image processing. International Journal of Computer Science Issues (IJCSI), 9(5), 269-276.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.