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Research on the application of deep learning methods in identification localization and recognition of engineering drawings
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Xin XIAO1, Qingsong CHEN1, Siyuan WU2, Rui HU1
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5) : 696 - 707
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Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition) | 2025, 37(5): 696-707
• Artificial Intelligenceand Big Data •
Research on the application of deep learning methods in identification localization and recognition of engineering drawings
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Xin XIAO1, Qingsong CHEN1, Siyuan WU2, Rui HU1
Affiliations
  • 1Digital Center, CISDI Engineering Co., Ltd., Chongqing 401122, P. R. China
  • 2School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
doi: 10.3979/j.issn.1673-825X.202408260226
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The localization and recognition of key symbols in engineering drawings have long been essential applications in computer vision. Compared with traditional methods, deep learning-based text detection approaches offer higher detection efficiency and accuracy. It is therefore necessary to apply existing text detection algorithms to engineering drawing recognition tasks. This paper proposes a deep learning-based method for the localization and recognition of key symbols in engineering drawings, focusing on the detection and recognition of index symbols and dimension symbols. For index symbol localization, the drawings are cropped to a uniform size, and non-maximum suppression is used to remove redundant candidate boxes. For dimension symbol localization, a complete detection is performed on the masked drawings, and the intersection-over-union between each detected box and index symbol location is calculated to filter out partial data. Experimental results demonstrate that the proposed method achieves high precision and recall in both the localization and recognition of index and dimension symbols in engineering drawings.

engineering drawings  /  object detection  /  character recognition  /  deep learning
Xin XIAO, Qingsong CHEN, Siyuan WU, Rui HU. Research on the application of deep learning methods in identification localization and recognition of engineering drawings[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2025 , 37 (5) : 696 -707 . DOI: 10.3979/j.issn.1673-825X.202408260226
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Year 2025 volume 37 Issue 5
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Article Info
doi: 10.3979/j.issn.1673-825X.202408260226
  • Receive Date：2024-08-26
  • Online Date：2026-04-16
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  • Received：2024-08-26
  • Revised：2025-06-20
Affiliations
    1Digital Center, CISDI Engineering Co., Ltd., Chongqing 401122, P. R. China
    2School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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