@article{(Open Science Index):https://publications.waset.org/pdf/10010637,
	  title     = {Non-Invasive Data Extraction from Machine Display Units Using Video Analytics},
	  author    = {Ravneet Kaur and  Joydeep Acharya and  Sudhanshu Gaur},
	  country	= {},
	  institution	= {},
	  abstract     = {Artificial Intelligence (AI) has the potential to transform
manufacturing by improving shop floor processes such as production,
maintenance and quality. However, industrial datasets are notoriously
difficult to extract in a real-time, streaming fashion thus, negating
potential AI benefits. The main example is some specialized industrial
controllers that are operated by custom software which complicates
the process of connecting them to an Information Technology (IT)
based data acquisition network. Security concerns may also limit
direct physical access to these controllers for data acquisition.
To connect the Operational Technology (OT) data stored in these
controllers to an AI application in a secure, reliable and available
way, we propose a novel Industrial IoT (IIoT) solution in this paper.
In this solution, we demonstrate how video cameras can be installed
in a factory shop floor to continuously obtain images of the controller
HMIs. We propose image pre-processing to segment the HMI into
regions of streaming data and regions of fixed meta-data. We then
evaluate the performance of multiple Optical Character Recognition
(OCR) technologies such as Tesseract and Google vision to recognize
the streaming data and test it for typical factory HMIs and realistic
lighting conditions. Finally, we use the meta-data to match the OCR
output with the temporal, domain-dependent context of the data to
improve the accuracy of the output. Our IIoT solution enables reliable
and efficient data extraction which will improve the performance of
subsequent AI applications.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {13},
	  number    = {7},
	  year      = {2019},
	  pages     = {423 - 428},
	  ee        = {https://publications.waset.org/pdf/10010637},
	  url   	= {https://publications.waset.org/vol/151},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 151, 2019},