Abstract

The advent of digital technology has led to significant changes in work processes in many organizations and industries worldwide including Thailand. This work aims to apply digital technology to improve measurement and metrology in terms of accuracy, productivity, and variability. We developed machine vision software for monitoring and collecting digital data from measurement systems. The application of Optical Character Recognition (OCR) technology in metrology is the first step toward digitalization, enabling enterprises to improve their measurement processes by automating data collection, analysis, and management. Moreover, it can be integrated with machine learning algorithms to enhance the automation of complex measurement tasks. To achieve this goal, an OCR software for data recording was designed in the LabVIEW environment. The image captured by the camera is processed by the LabVIEW OCR system which is trained continuously until it can recognize characters consistently and accurately. This software has been tested with the calibration systems which has a seven-segment digital format. The result shows that the software can acquire and transmit the instrument reading with reliable and exceptional accuracy. In addition, it can also reduce the steps and time required for measurement. In conclusion, the application of OCR technology can improve the efficiency, productivity, and accuracy of measurement. It also has a promising potential to enable remote measurement and calibration according to ISO/IEC 17025 possible.

Details

Title
Design and development of OCR software for remote measurement and calibration
Author
Nanna, N 1 ; Chanthawong, N 2 ; Buajarern, J 2 

 Digital Transformation Centre, National Institute of Metrology (Thailand) , Pathumthani 12120 , Thailand 
 Digital Transformation Centre, National Institute of Metrology (Thailand) , Pathumthani 12120 , Thailand; Dimensional Metrology Department, National Institute of Metrology (Thailand) , Pathumthani 12120 , Thailand 
First page
012012
Publication year
2023
Publication date
Dec 2023
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2906328542
Copyright
Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.