Abstract
With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components. The major problem that deters this area of research is the lacking of realistic text on chips datasets to act as a strong foundation. Hence, a text on chips dataset, ICText is used as the main target for the proposed Robust Reading Challenge on Integrated Circuit Text Spotting and Aesthetic Assessment (RRC-ICText) 2021 to encourage the research on this problem. Throughout the entire competition, we have received a total of 233 submissions from 10 unique teams/individuals. Details of the competition and submission results are presented in this report.
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References
Anitha, D.B., Rao, M.: A survey on defect detection in bare PCB and assembled PCB using image processing techniques. In: WiSPNET 2017, pp. 39–43 (2017)
Bolya, D., Foley, S., Hays, J., Hoffman, J.: Tide: a general toolbox for identifying object detection errors. arXiv preprint arXiv:2008.08115 (2020)
Chen, X., Jin, L., Zhu, Y., Luo, C., Wang, T.: Text recognition in the wild: a survey. ACM Comput. Surv. 54(2) (2021). https://doi.org/10.1145/3440756
Chng, C.K., et al.: ICDAR 2019 robust reading challenge on arbitrary-shaped text - RRC-ArT. In: ICDAR 2019, pp. 1571–1576 (2019)
Deloitte: Semiconductors - the next wave opportunities and winning strategies for semiconductor companies (2020). https://www2.deloitte.com/cn/en/pages/technology-media-and-telecommunications/articles/semiconductors-the-next-wave-2019.html
NVIDIA Developer: Jetson nano: deep learning inference benchmarks (2019). https://developer.nvidia.com/embedded/jetson-nano-dl-inference-benchmarks
Gomez, R., et al.: ICDAR 2017 robust reading challenge on coco-text. In: ICDAR 2017, pp. 1435–1443. IEEE (2017)
Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: CVPR 2016 (2016)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV 2017, pp. 2980–2988 (2017). https://doi.org/10.1109/ICCV.2017.322
Jocher, G.: ultralytics/yolov5 (2021). https://doi.org/10.5281/zenodo.3908559
Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: ICDAR (2015)
Karatzas, D., et al.: ICDAR 2013 robust reading competition. In: ICDAR (2013)
Kleber, S., Nölscher, H.F., Kargl, F.: Automated PCB reverse engineering. In: 11th USENIX Workshop on Offensive Technologies (WOOT 17) (2017)
Li, W., Neullens, S., Breier, M., Bosling, M., Pretz, T., Merhof, D.: Text recognition for information retrieval in images of printed circuit boards. In: IECON, pp. 3487–3493 (2014)
Lin, C.H., Wang, S.H., Lin, C.J.: Using convolutional neural networks for character verification on integrated circuit components of printed circuit boards. Appl. Intell. 49(11), 4022–4032 (2019). https://doi.org/10.1007/s10489-019-01486-5
Lin, T., et al.: Microsoft COCO: common objects in context. CoRR abs/1405.0312 (2014). http://arxiv.org/abs/1405.0312
Long, S., He, X., Ya, C.: Scene text detection and recognition: the deep learning era. arXiv preprint arXiv:1811.04256 (2018)
Munisankar, S.N.N., Rao, B.N.K.: Defect detection in printed board circuit using image processing. IJITEE 9(2), 3005–3010 (2019)
Nava-Dueñas, C.F., González-Navarro, F.F.: OCR for unreadable damaged characters on PCBs using principal component analysis and Bayesian discriminant functions. In: CSCI 2015, pp. 535–538 (2015)
Nayef, N., et al.: ICDAR 2017 robust reading challenge on multi-lingual scene text detection and script identification-RRC-MLT. In: ICDAR 2017, vol. 1, pp. 1454–1459. IEEE (2017)
Nazeri, K., Ng, E., Joseph, T., Qureshi, F., Ebrahimi, M.: EdgeConnect: structure guided image inpainting using edge prediction. In: ICCV 2019 (2019)
OpenVINO: Get a deep learning model performance boost with intel\(\textregistered \) platforms - openvino\(^{{\rm TM}}\) toolkit (2021). https://docs.openvinotoolkit.org/latest/openvino_docs_performance_benchmarks.html#intel-ncs2
Pramerdorfer, C., Kampel, M.: A dataset for computer-vision-based PCB analysis. In: MVA 2015, pp. 378–381 (2015)
Ray, S., Mukherjee, J.: A hybrid approach for detection and classification of the defects on printed circuit board. IJCA 121(12), 42–48 (2015)
Sun, Y., et al.: ICDAR 2019 competition on large-scale street view text with partial labeling - RRC-LSVT. In: ICDAR 2019, pp. 1557–1562 (2019)
Tang, S., He, F., Huang, X., Yang, J.: Online PCB defect detector on a new PCB defect dataset. CoRR abs/1902.06197 (2019). http://arxiv.org/abs/1902.06197
Wang, Q., Li, P., Zhu, L., Niu, Y.: 1st place solution to ICDAR 2021 RRC-ICTEXT end-to-end text spotting and aesthetic assessment on integrated circuit. arXiv preprint arXiv:2104.03544 (2021)
Wu, L., et al.: Editing text in the wild. In: ACMMM 2019, pp. 1500–1508 (2019)
Zhang, Q., et al.: Deep learning based defect detection for solder joints on industrial x-ray circuit board images. arXiv preprint arXiv:2008.02604 (2020)
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Ng, C.C. et al. (2021). ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_44
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