Detecting Handwritten Line for TikZ Code Generating

Authors

  • Nataliia Shapoval National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” https://orcid.org/0000-0002-8509-6886
  • Denys Lobanov National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.76.17671

Keywords:

hough transform, convolutional neural networks, line detection

Abstract

This work is devoted to the recognition of straight lines in geometric drawings created by hand with the help of neural networks. The LaTeX language is usually used for the design of articles, which takes more time, especially when writing formulas or constructing geometric drawings, unlike writing by hand. Automating the construction of drawings will make it possible to speed up the process of writing articles. The paper considers the recognition of straight lines as the most popular element of geometric drawings. The Hough transform for straight lines detection and its disadvantages are considered. The use of convolutional neural networks for this task is proposed as the best tool for working with images. To train the model, a dataset was created with handwritten lines and lines constructed in a graphics editor. The results of the neural network are given.

Author Biographies

Nataliia Shapoval , National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Candidate of Sciences (Engineering)

Denys Lobanov , National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Bachelor's degree student

Department of Artificial Intelligence

Institute of Applied Systems Analysis

References

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ANG Zelun and LIU Jyh-Charn, “Translating math formula images to LaTeX sequences using deep neural networks with sequence-level training,”. International Journal on Document Analysis and Recognition (IJDAR), 2021, 24.1–2: 63–75. https://doi.org/10.1007/s10032-020-00360-2

HEROUT Adam, et al., “Review of Hough transform for line detection,” Real-Time Detection of Lines and Grids: By PClines and Other Approaches, 2013, 3–16. https://doi.org/10.1007/978-1-4471-4414-4_2

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Published

2023-06-23

How to Cite

Shapoval , N., & Lobanov , D. (2023). Detecting Handwritten Line for TikZ Code Generating. Electronics and Control Systems, 2(76), 56–60. https://doi.org/10.18372/1990-5548.76.17671

Issue

Vol. 2 No. 76 (2023)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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