Text Detection and Recognition for X-ray Weld Seam Images
Source abstract
Abstract
X-ray weld seam images carry vital information about welds. Leveraging graphic–text recognition technology enables intelligent data collection in complex industrial environments, promising significant improvements in work efficiency. This study focuses on using deep learning methods to enhance the accuracy and efficiency of detecting weld seam information. We began by actively gathering a dataset of X-ray weld seam images for model training and evaluation. The study comprises two main components: text detection and text recognition. For text detection, we employed a model based on the DBNet algorithm and tailored post-processing techniques to the unique features of weld seam images. Through model training, we achieved efficient detection of the text regions, with 91% precision, 92.4% recall, and a 91.7% F1 score on the test dataset. In the text recognition phase, we introduced modules like CA, CBAM, and HFA to capture the character position information and global text features effectively. This optimization led to a remarkable text line recognition accuracy of 93.4%. In conclusion, our study provides an efficient deep learning solution for text detection and recognition in X-ray weld seam images, offering robust support for weld seam information collection in industrial manufacturing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Handwritten Text Recognition Techniques · Image Processing and 3D Reconstruction · Vehicle License Plate Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information, Yunnan Normal University, Kunming 650500, China
Funding
Yunnan Provincial Agricultural Basic Research Joint Special Project · Yunnan Ten-Thousand Talents Program
References (33)
Show source-provided references
1. Tian, Z., Huang, W., He, T., He, P., and Qiao, Y. (2016). Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11–14 October 2016, Proceedings, Part VIII 14, Springer.
2. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
3. Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., and Shao, S. (2019, January 15–20). Shape robust text detection with progressive scale expansion network. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00956 ↗
4. Zhu, Y., Chen, J., Liang, L., Kuang, Z., Jin, L., and Zhang, W. (2021, January 20–25). Fourier contour embedding for arbitrary-shaped text detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.00314 ↗
5. Liao, M., Wan, Z., Yao, C., Chen, K., and Bai, X. (2020, January 7–12). Real-time scene text detection with differentiable binarization. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6812 ↗
6. Real-time scene text detection with differentiable binarization and adaptive scale fusion10.1109/TPAMI.2022.3155612 ↗
7. Aster: An attentional scene text recognizer with flexible rectification10.1109/TPAMI.2018.2848939 ↗
8. Li, H., Wang, P., Shen, C., and Zhang, G. (February, January 27). Show, attend and read: A simple and strong baseline for irregular text recognition. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
9. Master: Multi-aspect non-local network for scene text recognition10.1016/j.patcog.2021.107980 ↗
10. Fang, S., Xie, H., Wang, Y., Mao, Z., and Zhang, Y. (2021, January 20–25). Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.00702 ↗
11. Na, B., Kim, Y., and Park, S. (2022, January 23–27). Multi-modal text recognition networks: Interactive enhancements between visual and semantic features. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.10.1007/978-3-031-19815-1_26 ↗
12. Liu, W., Chen, C., Wong, K.-Y.K., Su, Z., and Han, J. (2016, January 19–22). Star-net: A spatial attention residue network for scene text recognition. Proceedings of the BMVC, York, UK.10.5244/C.30.43 ↗
13. Sun, Y., Zhang, C., Huang, Z., Liu, J., Han, J., and Ding, E. (2019). Proceedings of the Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, 2–6 December 2018, Revised Selected Papers, Part III 14, Springer.
14. Lyu, P., Liao, M., Yao, C., Wu, W., and Bai, X. (2018, January 8–14). Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01264-9_5 ↗
15. Liao, M., Pang, G., Huang, J., Hassner, T., and Bai, X. (2020). Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, 23–28 August 2020, Proceedings, Part XI 16, Springer.
16. Yue, X., Kuang, Z., Lin, C., Sun, H., and Zhang, W. (2020, January 23–28). Robustscanner: Dynamically enhancing positional clues for robust text recognition. Proceedings of the European Conference on Computer Vision, Glasgow, UK.10.1007/978-3-030-58529-7_9 ↗
17. Hou, Q., Zhou, D., and Feng, J. (2021, January 20–25). Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01350 ↗
18. Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
19. Abinet++: Autonomous, bidirectional and iterative language modeling for scene text spotting10.1109/TPAMI.2022.3223908 ↗
20. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
21. The quickhull algorithm for convex hulls10.1145/235815.235821 ↗
22. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30.
23. Multimodal transformer for unaligned multimodal language sequences
24. Ronneberger, O., Fischer, P., and Brox, T. (2015). Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Proceedings, Part III 18, Springer.
25. Bautista, D., and Atienza, R. (2022, January 23–27). Scene text recognition with permuted autoregressive sequence models. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.10.1007/978-3-031-19815-1_11 ↗
26. Jaderberg, M., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014). Synthetic data and artificial neural networks for natural scene text recognition. arXiv.
27. ICDAR 2003 robust reading competitions: Entries, results, and future directions10.1007/s10032-004-0134-3 ↗
28. Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Bigorda, L.G.I., Mestre, S.R., Mas, J., Mota, D.F., Almazàn, J.A., and de las Heras, L.P. (2013, January 25–28). ICDAR 2013 robust reading competition. Proceedings of the 2013 12th International Conference on Document Analysis and Recognition, Washington, DC, USA.10.1109/ICDAR.2013.221 ↗
29. Contextual transformer networks for visual recognition10.1109/TPAMI.2022.3164083 ↗
30. Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., and Huang, Z. (2023, January 4–10). Efficient Multi-Scale Attention Module with Cross-Spatial Learning. Proceedings of the ICASSP 2023—2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.10.1109/ICASSP49357.2023.10096516 ↗
31. Res2net: A new multi-scale backbone architecture10.1109/TPAMI.2019.2938758 ↗
32. Lin, F., Yuan, J., Wu, S., Wang, F., and Wang, Z. (November, January 29). UniNeXt: Exploring A Unified Architecture for Vision Recognition. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada.
33. Chen, J., Kao, S.-H., He, H., Zhuo, W., Wen, S., Lee, C.-H., and Gary Chan, S.-H. (2023, January 17–24). Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.01157 ↗