PRINTING DEFECT IDENTIFICATION IN PHARMACEUTICAL BLISTERS USING IMAGE PROCESSING
DOI:
https://doi.org/10.22159/ajpcr.2018.v11i3.23407Keywords:
Blisters, Print defects, Image processing, Labels, SegmentationAbstract
 Objective: Our aim is to detect printing defects in pharmaceutical tablets from the manufacturing line using image processing techniques.
Methods: The printed labels contain the details of the chemical composition, date of manufacture, date of expiry, manufacturing location, etc., images of the labels are obtained and processed using image processing algorithms to detect any defects on the labels before dispatch.
Results: The printing defects on the labels such as missing letters, words, lines, and disorientation of alignments.
Conclusion: Euclidean distance method was used for comparison that yielded 95% accuracy in removing tablets with printing defects.
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References
Sanket S, Garg SK. Fast dissolving tablets (FDTs): Current status, new market opportunities, recent advances in manufacturing technologies and future prospects. Int J Pharm Pharm Sci 2014;6:22-35.
Ketan S, Anuradha G, Jignasa S. Modified formulation of febuxostat: Improved efficacy and safety. Int J Pharm Pharm Sci 2015;8:359-66.
Wang W, Jiang Z, Ping Z, Wei C, Xuan Z. Color printed image defect detection based on the image feature matches. Biotechnol Indian J 2014;10:12620-7.
Broggi A. Robust Real-Time Lane and Road Detection in Critical Shadow conditions. Proceedings of International Symposium on Computer Vision-ISCV; 1995. p. 353-8.
Shankar NG, Ravi N, Hong W. On-Line Defect Detection in Web Offset Printing. Montreal, Que., Canada: 2003 4th International Conference on Control and Automation Proceedings; 2003. p. 794-8.
Deutschl E, Gasser C, Niel A, Werschoning J. Defect detection on rail surface by a vision based system. IEEE Intelligent Vehicles Symposium; 2004. p. 507-11.
Li W, Duan S, Yin Z. Image segmentation based on facet model fitting. Adv Mater Res 2012; 459:35-9.
Cavdarli M, Seke E. Measuring Roughness on Wood Surfaces for Detection of Defects Using Multi-Frames Imaging. Available from: http://www.eseke.ogu.edu.tr/pubs/inista2010Wood.pdf.
Kumbhar PY, Mathpati T, Kamaraddi R, Kshirsagar N. Textile fabric defects detection and sorting using image processing. Int J Res Emerg Sci Technol 2016;3:19-24.
Rizvi1 SA, Neelima KB, Saravanan T. Image processing based defect detection of printed circuit board. Int J Res Appl Sci Eng Technol 2015;4:819-32.
Dave N, Tambade V, Pandhare B, Saurav S. PCB defect detection using processing and embedded system. Int Res J Eng Technol 2016;3:1897 901.
Karthik D, Vijayarekha K, Vinodha D. A simple model for skin disease identification using image processing. Res J Pharm Biol Chem Sci 2016;7:2758-61.
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