Symbol Spotting in Electronic Images Using Morphological Filters and Hough Transform
DOI:
https://doi.org/10.25271/sjuoz.2022.10.3.874Keywords:
Symbol spotting, Hough Transform, Electronic Symbols, Morphological filtersAbstract
In this paper, two algorithms (a preliminary and enhanced algorithm) to detect electronic symbols in document images are proposed. Morphological operations coupled with Hough transform are used in the proposed methodology. The objective of the proposed algorithms is to detect electronic symbols of open and closed shapes. The methods can successfully spot many complex types of electronic symbols such as Fixed Resistor; Zener Diode; Pn Junction Diode; NPN transistor; Not Gate; Input and Output Terminals; Ground; Single Cell Battery; Transformer; and LED symbols. The experimental results on the Systems Evaluation Synthetic Documents (SESYD) dataset show that the proposed preliminary method detects 86.2%, (12151 symbols out of 14100 symbols), precision of 0.94, recall of 0.91, and F-measure of 0.92. An enhanced algorithm that used line Hough transform is also demonstrated with accuracy of 91.2% (12864 symbols out of 14100 symbols), precision of 0.97, recall of 0.93, and F-measure of 0.95).
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Figure 25: Undetected and miss detected symbols of preliminary algorithm shown wrapped in blue boxes.
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Figure 25: Undetected and miss detected symbols of preliminary algorithm shown wrapped in blue boxes.
Datta, R., Mandal, S., & Biswas, S. (2019). Automatic Abstraction of Combinational Logic Circuit from Scanned Document Page Images. Pattern Recognition and Image Analysis, 29(2), 212– 223.
Delalandre, M. (2012). Systems Evaluation SYnthetic Documents. Mathieu.delalandre.free.fr.
http://mathieu.delalandre.free.fr/projects/sesyd/
Delalandre, M. (2016). generation of graphical ground Truth.
Mathieu.delalandre.free.fr. http://mathieu.delalandre.free.fr/projects/3gT.html
D.S. Ramadhan & H.S.M. Al-Khaffaf \ Science Journal of University of Zakho 10(3), 119-129, July-September 2022
Edwards, B., & Chandran, V. (2000). Machine recognition of hand- drawn circuit diagrams. 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
Efford, N. (2000). Digital image processing : a practical introduction using Java. Addison-Wesley.
Fan, Z., Zhu, L., Li, H., Chen, X., Zhu, S., & Tan, P. (2021). FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting. ArXiv:2105.07147 [Cs].
KaiBin, O., & Hooi, Y. K. (2021). Critical Literature Review of Named Entity Recognition in Symbol Spotting. 2021 International Conference on Computer & Information Sciences (ICCOINS).
Moetesum, M., Waqar Younus, S., Ali Warsi, M., & Siddiqi, I. (2018). Segmentation and Recognition of Electronic Components in Hand-Drawn Circuit Diagrams. ICST Transactions on Scalable Information Systems, 5(16), 154478. https://doi.org/10.4108/eai.13-4-2018.154478
Müller, S., & Rigoll, G. (2000). Engineering Drawing Database Retrieval Using Statistical Pattern Spotting Techniques. Graphics Recognition Recent Advances, 246–255.
Najman, L., Gibot, O., & Barbey, M. B. L. (2001). Automatic Title Block Location in Technical Drawings.
Qureshi, R. J., Ramel, J.-Y., Barret, D., & Cardot, H. (2008). Spotting Symbols in Line Drawing Images Using Graph
Representations. Lecture Notes in Computer Science, 91–103.
https://doi.org/10.1007/978-3-540-88188-9_10
Rezvanifar, A., Cote, M., & Albu, A. B. (2020). Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-
based Framework.
Rezvanifar, A., Cote, M., & Albu, A. B. (2021). Geometry-based symbol
spotting in born-digital architectural floor plans. Journal of
Electronic Imaging, 30(04).
Rezvanifar, A., Cote, M., & Branzan Albu, A. (2019). Symbol spotting
for architectural drawings: state-of-the-art and new industry- driven developments. IPSJ Transactions on Computer Vision and Applications, 11(1).
Tabbone, S., Wendling, L., & Zuwala, D. (2004). A Hybrid Approach to Detect Graphical Symbols in Documents. Document Analysis Systems VI, 342–353.
Zhang, W., & Liu, W. (2007). A New Vectorial Signature for Quick Symbol Indexing, Filtering and Recognition. Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).
Zuwala, D., & Tabbone, S. (2006). A Method for Symbol Spotting in Graphical Documents. Document Analysis Systems VII, 518– 528.
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Published
2022-08-24
How to Cite
Ramadhan, D. S., & Al-Khaffaf, H. S. M. (2022). Symbol Spotting in Electronic Images Using Morphological Filters and Hough Transform. Science Journal of University of Zakho, 10(3), 119–129. https://doi.org/10.25271/sjuoz.2022.10.3.874
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