Abstract
The text detection and recognition plays an important role in automatic management of electrical diagrams. However, the images of electrical diagrams often have high resolution, and the format of the text in them is also unique and densely distributed. These factors make the general-purpose text spotting models unable to detect and recognize the text effectively. In this paper, we propose a text spotting model based on improved PP-OCRv3 to achieve better performance on text spotting of electrical diagrams. Firstly, a region re-segmentation module based on pixel line clustering is designed to correct detection errors on irregularly shaped text containing vertical and horizontal characters. Secondly, an improved BiFPN module with channel attention and depthwise separable convolution is introduced during text feature extracting to improve the robustness of input images with different scales. Finally, a character re-identification module based on region extension and cutting is added during the text recognition to reduce the adverse effects of simple and dense character on the model. The experimental results show that our model has better performance than the state-of-the-art (SOTA) methods on the electrical diagrams data sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jamieson, L., Moreno-Garcia, C.F., Elyan, E.: Deep learning for text detection and recognition in complex engineering diagrams. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2020)
Shanbin, L., Haoyu, W., Junhao, Z.: Electrical cabinet wiring detection method based on improved yolov5 and pp-ocrv3. In: 2022 China Automation Congress (CAC), pp. 6503–6508. IEEE (2022)
Li, C., et al.: Pp-ocrv3: more attempts for the improvement of ultra lightweight OCR system. arXiv preprint arXiv:2206.03001 (2022)
Wang, W., et al.: Shape robust text detection with progressive scale expansion network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9336–9345 (2019)
Wang, W., et al.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8440–8449 (2019)
Liao, M., Wan, Z., Yao, C., Chen, K., Bai, X.: Real-time scene text detection with differentiable binarization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11474–11481 (2020)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 27 (2014)
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2016)
Sheng, F., Chen, Z., Xu, B.: NRTR: a no-recurrence sequence-to-sequence model for scene text recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 781–786. IEEE (2019)
Du, Y., et al.: SVTR: scene text recognition with a single visual model. arXiv preprint arXiv:2205.00159 (2022)
Qiao, L., et al.: Mango: a mask attention guided one-stage scene text spotter. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2467–2476 (2021)
Liao, M., Pang, G., Huang, J., Hassner, T., Bai, X.: Mask TextSpotter v3: segmentation proposal network for robust scene text spotting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 706–722. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_41
Wang, P., et al.: Pgnet: real-time arbitrarily-shaped text spotting with point gathering network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2782–2790 (2021)
Liu, Y., et al.: Abcnet v2: adaptive Bezier-curve network for real-time end-to-end text spotting. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 8048–8064 (2021)
Huang, M., et al.: Swintextspotter: scene text spotting via better synergy between text detection and text recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4593–4603 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, Y., Zhang, D., Sun, C. (2024). Text Spotting of Electrical Diagram Based on Improved PP-OCRv3. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_10
Download citation
DOI: https://doi.org/10.1007/978-981-99-8181-6_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8180-9
Online ISBN: 978-981-99-8181-6
eBook Packages: Computer ScienceComputer Science (R0)