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DOI: 10.14569/IJACSA.2022.0130563
PDF

Enhanced Symbol Recognition based on Advanced Data Augmentation for Engineering Diagrams

Author 1: Ong Kai Bin
Author 2: Yew Kwang Hooi
Author 3: Said Jadid Abdul Kadir
Author 4: Haruhiro Fujita
Author 5: Luqman Hakim Rosli

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

  • Abstract and Keywords
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Abstract: Symbol recognition has generated research interest for image analytics of engineering diagrams. Techniques including structural, syntactic, statistical, Convolution Neural Network (CNN) were studied to identify gaps of research. Despite popularity, CNN requires huge learning dataset, which often involves costly procurement. To address this, combination between CycleGAN and CNN is proposed. CycleGAN generates more learning dataset synthetically, thus yielding opportunity to improve accuracy of symbol recognition. In the domain of for engineering symbols, standard CNN model is developed and used in experimental testing. Different ratios of training dataset were tested in multiple experiments using Piping and Instrument Diagram (P&IDs) drawings. Result of highest accuracy for symbol recognition is up to 92.85% against baseline and other method. The results determined that gradual reduction of training samples, the effectiveness of recognition accuracy performance after using proposed method was remained substantially stable.

Keywords: Symbol recognition; symbol spotting; engineering drawing; convolution neural network (CNN); CycleGAN; piping and instrument diagram (P&ID)

Ong Kai Bin, Yew Kwang Hooi, Said Jadid Abdul Kadir, Haruhiro Fujita and Luqman Hakim Rosli, “Enhanced Symbol Recognition based on Advanced Data Augmentation for Engineering Diagrams” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130563

@article{Bin2022,
title = {Enhanced Symbol Recognition based on Advanced Data Augmentation for Engineering Diagrams},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130563},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130563},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Ong Kai Bin and Yew Kwang Hooi and Said Jadid Abdul Kadir and Haruhiro Fujita and Luqman Hakim Rosli}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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