Abstract
The recognition of image text sequences has been the subject of long-term research in computer vision. The recognition problem of the shell number image sequence studied in this paper is based on the deep convolutional neural network architecture, and an image recognition algorithm that integrates feature extraction, sequence modeling, and transcription into a unified framework is adopted. The algorithm used has four characteristics: (1) Compared with most algorithms that require separate training and coordination, the method adopted in this paper has end-to-end characteristics. (2) It can handle indefinite length image sequences without involving character segmentation or horizontal-scale normalization. (3) The algorithm is not limited to any predefined vocabulary, and has achieved remarkable performance in both no-lexicon and dictionary-based scene text recognition tasks. (4) The algorithm produces an efficient and much smaller model, which is more practical for real-world scenarios. Using this algorithm for bay number recognition can assist in scene location and provide smart services and application through AI technology.
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References
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25(2), 1097–1105 (2012)
Graves, A., Liwicki, M., Santiago, F., et al.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)
Su, B., Lu, S.: Accurate scene text recognition based on recurrent neural network. In Asian Conference on Computer Vision, Springer, Cham, pp. 35–48 (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 (2015)
Redmon, J., Divvala, S., Girshick, R. et al.: You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement (2018)
Acknowledgements
This work was financially supported by fund project, that is, Guangzhou Institute of industry and commerce college level research project in 2019 “Research and design of S band radio frequency front-end” 2019KQNCX231.
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Li, X., Duan, C., Yin, P., Zhi, Y., Li, N. (2021). Bay Number Recognition Based on Deep Convolutional Recurrent Neural Network. In: Kountchev, R., Mahanti, A., Chong, S., Patnaik, S., Favorskaya, M. (eds) Advances in Wireless Communications and Applications. Smart Innovation, Systems and Technologies, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-15-5697-5_23
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DOI: https://doi.org/10.1007/978-981-15-5697-5_23
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