Abstract
This paper proposes a new real-time Laplacian of Gaussian (RT-LoG) operator for scene text detection. This method takes advantage of the Gaussian kernel distribution in the spatial/scale-space domains and kernel decomposition with the box filtering method. Two levels of optimization are given. The first level of optimization within the spatial domain is obtained by box mutualization. The second level of optimization within the spatial/scale-space domains is performed using a mixed method for box selection. The proposed RT-LoG operator is evaluated on the ICDAR2017 RRC-MLT dataset in terms of robustness and time processing. The results are compared with the state-of-the-art real-time operators for scene text detection. The proposed operator appears as the top performance with the best trade-off between robustness and time processing. The proposed operator can support approximately 30 frames per second (FPS) up to the Quad-HD resolution on a regular CPU architecture with a low-level latency. In addition, the proposed operator can support the full pipeline for scene text detection. Our system is competitive with the top accurate systems of the literature while processing with a difference of two orders of magnitude in term of processing resources.
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Notes
In practice, \(k \in ]1, \sqrt{2}]\).
For simplification, considering the 1D case.
Single Precision.
References
Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. PAMI 37(7), 1480–1500 (2015)
Long, S., He, X., Ya, C.: Scene text detection and recognition: the deep learning era, arXiv:1811.04256 (2018)
Nayef, N., Yin, F., Bizid, I., Choi, H.: ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-RRC-MLT. ICDAR (2017). https://doi.org/10.1109/ICDAR.2017.237
Neumann, L., Matas, J.: Real-time lexicon-free scene text localization and recognition. PAMI 38(9), 1872–1885 (2016)
Buttazzo, G.C.: Hard real-time computing systems: predictable scheduling algorithms and applications. Springer Science & Business Media, Berlin (2011)
Rey-Otero, I., Morel, J.M.: An analysis of scale-space sampling in SIFT. ICIP (2014). https://doi.org/10.1109/ICIP.2014.7025982
Busta, M., Neumann, L., Matas, J.: Fastext: efficient unconstrained scene text detector. ICCV (2015). https://doi.org/10.1109/ICCV.2015.143
Cho, H., Sung, M., Jun, B.: Canny text detector: Fast and robust scene text localization algorithm. CVPR (2016). https://doi.org/10.1109/CVPR.2016.388
Epshtein, B., Ofek, E.: Detecting text in natural scenes with stroke width transform. CVPR (2010). https://doi.org/10.1109/CVPR.2010.5540041
Girones, X., Julia, C.: Real-time text localization in natural scene images using a linear spatial filter. ICDAR (2017). https://doi.org/10.1109/ICDAR.2017.208
Gomez, L., Karatzas, D.: MSER-based real-time text detection and tracking. ICPR (2014). https://doi.org/10.1109/ICPR.2014.536
Turki, H., Halima, M.B., Alimi, A.: Text detection based on MSER and CNN features. ICDAR (2017). https://doi.org/10.1109/ICDAR.2017.159
Zhao, R., Niu, X., Wu, Y., Luk, W., Liu, Q.: Optimizing CNN-based object detection algorithms on embedded FPGA platforms. ISARC (2017). https://doi.org/10.1007/978-3-319-56258-2_22
Maceina, T.J., Manduchi, G.: Assessment of general purpose GPU systems in real-time control. TNS 64(6), 1455–1460 (2017)
Kim, H., Nam, H., Jung, W., Lee, J.: Performance analysis of CNN frameworks for GPUs. ISPASS (2017). https://doi.org/10.1109/ISPASS.2017.7975270
Wang, F., Zhao, L., Li, X., Wang, X.: Geometry-aware scene text detection with instance transformation network. CVPR (2018). https://doi.org/10.1109/CVPR.2018.00150
Fragoso, V., Srivastava, G., Nagar, A., Li, Z.: Cascade of box (CABOX) filters for optimal scale space approximation. CVPR (2014). https://doi.org/10.1109/CVPRW.2014.24
Liu, Y., Zhang, D., Zhang, Y.: Real-time scene text detection based on stroke model. ICPR (2014). https://doi.org/10.1109/ICPR.2014.537
Nguyen, D.C., Delalandre, M., Conte, D., Pham, T.A.: Performance evaluation of real-time and scale-invariant LoG operators for text detection. VISAPP (2019). https://doi.org/10.5220/0007361503440353
Lindeberg, T.: Scale-space theory: a basic tool for analysing structures at different scales. JAS 21, 224–270 (1994)
Charalampidis, D.: Recursive implementation of the Gaussian filter using truncated cosine functions. TIP 64(14), 3554–3565 (2016)
Elboher, E., Werman, M.: Efficient and accurate Gaussian image filtering using running sums. ISDA 897–902, (2011)
Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)
Strang, G.: Introduction to Linear Algebra, 5th edn. Cambridge Press, Cambridge (1993)
Karatzas, D., Gomez-Bigorda, L.: ICDAR 2015 competition on robust reading. ICDAR 1156–1160, (2015)
Siddhesh, K., Amit, A.: Faster K-Means Cluster Estimation, arXiv, vol.1701.04600 (2017)
Medioni, G.G., Lim, J., Park, J.: Text segmentation in color images using tensor voting. Image Vis Comput IVC 25.5, 671–685 (2007)
Mao, J., Li, H., Zhou, W., Yan, S., Tian, Q.: Scale based region growing for scene text detection. ACMMM (2013). https://doi.org/10.1145/2502081.2502108
Zhu, W., Lou, J., Chen, L., Xia, Q., Ren, M.: Scene text detection via extremal region based double threshold convolutional network classification. PLoS One 12(8), e0182227 (2017)
Yin, X.C., Pei, W.Y., Zhang, J.: Multi-orientation scene text detection with adaptive clustering. PAMI 37(9), 1930–1937 (2015)
Dai, J., Wang, Z., Zhao, X., Shao, S.: Scene text detection based on enhanced multi-channels MSER and a fast text grouping process. ICCCBDA (2018). https://doi.org/10.1109/ICCCBDA.2018.8386541
Nguyen, C., Delalandre, M., Conte, D., Pham, T.: Fast scene text detection with RT-LoG operator and CNN. VISAPP, (2020)
Liu, J., Liu, X., Sheng, J., Liang, D.: Pyramid Mask Text Detector, arXiv preprint: arXiv:1903.11800 (2019)
He, W., Zhang, X.Y., Yin, F., Liu, C.L.: Multi-oriented and multi-lingual scene text detection with direct regression. TIP 27(11), 5406–5419 (2018)
Huang, Z., Zhong, Z., Sun, L., Huo, Q.: Mask R-CNN with pyramid attention network for scene text detection. WACV (2019). https://doi.org/10.1109/WACV.2019.00086
Zhang, C., Liang, B., Huang, Z., En, M., Han, J.: Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes, arXiv preprint: arXiv:1904.06535 (2019)
Lyu, P., Yao, C., Wu, W., Yan, S.: Multi-oriented scene text detection via corner localization and region segmentation. CVPR 7553–7563, (2018)
Liu, X., Liang, D., Yan, S., Chen, D.: Fots: Fast oriented text spotting with a unified network. CVPR 5676–5685, (2018)
Zhong, Z., Sun, L., Huo, Q.: An anchor-free region proposal network for faster r-cnn based text detection approaches, arXiv preprint: arXiv:1804.09003 (2018)
Wang, H., Rong, X., Tian, Y.: Towards accurate instance-level text spotting with guided attention. ICME (2019). https://doi.org/10.1109/ICME.2019.00175
Lyu, P., Liao, M., Yao, C., Wu, W.: Mask TextSpotter: an end-to-end trainable neural network for spotting text with arbitrary shapes, ECCV (2018)
Zhou, X., Yao, C., Wen, H., Wang, Y.: EAST: an efficient and accurate scene text detector. CVPR 5551–5560, (2017)
He, P., Huang, W., He, T., Zhu, Q.: Single shot text detector with regional attention. ICCV 3047–3055, (2017)
Miao, Z., Jiang, X.: Contrast invariant interest point detection by zero-norm log filter. TIP 25(1), 331–342 (2016)
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Nguyen Dinh, C., Delalandre, M., Conte, D. et al. Fast RT-LoG operator for scene text detection. J Real-Time Image Proc 18, 19–36 (2021). https://doi.org/10.1007/s11554-020-00942-7
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DOI: https://doi.org/10.1007/s11554-020-00942-7