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Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., JOHNSTON, P. and GARKUWA, B. 2020. Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207479. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207479

One of the key features of most document image digitisation systems is the capability of discerning between the main components of the printed representation at hand. In the case of engineering drawings, such as circuit diagrams, telephone exchanges... Read More about Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks..

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. (2020)
Conference Proceeding
ELYAN, E., MORENO-GARCÍA, C.F. and JOHNSTON, P. 2020. Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 215-224. Available from: https://doi.org/10.1007/978-3-030-48791-1_16

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents... Read More about Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks..