DOI: 10.18413/2518-1092-2024-9-3-0-6

APPROACH TO VECTORIZATION OF DRAWINGS OF DESIGN DOCUMENTATION ON PAPER

Oleg Olegovich Basov
Oleg Dmitrievich Demin
Dmitry Aleksandrovich Noskov

The work proposes a solution to the problem of vectorization and machine interpretation of drawings of design documentation on paper, which provides the ability to automate the transfer of images of products, parts and assembly units into CAD systems. A number of neural network architectures have been proposed for detecting and recognizing the main elements of a drawing (frame, title block, specification, views, projections and sections), inscriptions, dimension and extension lines, as well as primitives that directly describe the product. For hierarchical and interconnected vectorization, a mechanism for semantic segmentation of drawings based on a graph neural network is proposed. The results of the implementation of the main stages of solving the problem of vectorization of design drawings are presented.

Keywords: design documentation, drawing, product, vectorization, graph neural network, deep reinforcement learning.

Number of views: 139 (view statistics)

Количество скачиваний: 330

Full text (PDF)To articles list

Information for citation:

Basov O.O., Demin O.D., Noskov D.A. Approach to vectorization of drawings of design documentation on paper // Research result. Information technologies. – Т.9, №3, 2024. – P. 55-62. DOI: 10.18413/2518-1092-2024-9-3-0-6

User comments
Reference lists

While nobody left any comments to this publication.
You can be first.

Liu C., Wu J., Kohli P., Furukawa Y. Raster-to-vector: revisiting floorplan transformation. Proceedings of the IEEE International Conference on Computer Vision. 2017: 2195–2203. DOI: 10.1109/ICCV.2017.241.
Ellis K., Ritchie D., Solar-Lezama A., Tenenbaum J. Learning to infer graphics programs from hand-drawn images. Advances in neural information processing systems. 2018: 6059–6068.
Guo Y., Zhang Z., Han C., Hu W.B., Li C., Wong T.T. Deep line drawing vectorization via line subdivision and topology reconstruction. Comput. Graph. Forum 38, 2019: 81–90. DOI: 10.1111/cgf.13818.
Gao J., Tang C., Ganapathi-Subramanian V., Huang J., Su H., Guibas L.J. Deepspline: Data-driven reconstruction of parametric curves and surfaces. 2019. arXiv preprint arXiv:1901.03781.
Ha D., Eck D. A neural representation of sketch drawings. 2018. URL: https://openreview.net/pdf?id=Hy6GHpkCW.
Zhou T., Fang C., Wang Z., Yang J., Kim B., Chen Z., Brandt J., Terzopoulos D. Learning to doodle with stroke demonstrations and deep q-networks. BMVC. 2018: 13.
Kaiyrbekov K., Sezgin M. Stroke-based sketched symbol reconstruction and segmentation. 2019. arXiv preprint arXiv:1901.03427.
Zheng N., Jiang Y., Huang D. Strokenet: A neural painting environment. ICLR (Poster) 2019.
Ribeiro L.S.F., Bui T., Collomosse J., Ponti M., Sketchformer: Transformer-based representation for sketched structure. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020: 14 153–14 162.
Lopes R.G., Ha D., Eck D., Shlens J. A learned representation for scalable vector graphics. Proceedings of the IEEE/CVF International Conference on Computer Vision (CVPR). 2019: 7930–7939.
Carlier A., Danelljan M., Alahi A., Timofte R. Deepsvg: A hierarchical generative network for vector graphics animation. Advances in Neural Information Processing Systems. 2020, vol. 33: 16 351–16 361.
Azadi S., Fisher M., Kim V.G., Wang Z., Shechtman E., Darrell T. Multi-content gan for few-shot font style transfer. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). 2018: 7564–7573.
Gao Y., Guo Y., Lian Z., Tang Y., Xiao J. Artistic glyph image synthesis via one-stage few-shot learning. ACM Transactions on Graphics (TOG). 2019, vol. 38, no. 6: 1–12.
Li T.-M., Lukáč M., Gharbi M., Ragan-Kelley J. Differentiable vector graphics rasterization for editing and learning. ACM Transactions on Graphics (TOG). 2020, vol. 39, no. 6: pp. 1–15.
Hao S., Xuefeng L., Jianwei N., Jiahe C., Ji W., Xinghao W., Nana W. MARVEL: Raster Gray-level Manga Vectorization via Primitive-wise Deep Reinforcement Learning. IEEE Transactions on Circuits and Systems for Video Technology. 2023: 2677–2693. DOI: 10.1109/TCSVT.2023.3309786.

All journals

Send article

Research result. Information technologies is included in the scientific database of the RINTs (license agreement No. 765-12/2014 dated 08.12.2014).

Журнал включен в перечень рецензируемых научных изданий, рекомендуемых ВАК

The journal is indexed by the following scientific databases and platforms

Research Result. Research result. Information technologies (ISSN 2518-1092)

The journal materials and website are licensed under Creative Commons «Attribution» 4.0 International.

The Founder: Federal State Autonomous Educational Institution of Higher Education "Belgorod National Research University"The Founder’s address: 85 Pobedy Street, Belgorod, the Belgorod region, 308015, Russia

The Publisher: Federal State Autonomous Educational Institution of HigherEducation "Belgorod National Research University" The Founder’s address:85 Pobedy Street, Belgorod, the Belgorod region, 308015, Russia

Editors Office: chief editor Chernomorets Andrey Alekseevich, e-mail: RR_IT@bsuedu.ru, phone: +7 (4722) 30-13-92.

Registered by the Federal Service for Supervision of Communications, Information Technology and Mass Media (Roskomnadzor)

Certificate

Info letter (Russian)

Have questions?
You can write to us:

✉ Executive Secretary

✉ Site administration

✉ Content manager