Text Detection and Recognition for Robot Localization

Raisi, Z.; Zelek, J.

doi:10.22061/jecei.2023.9857.658

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	Text Detection and Recognition for Robot Localization
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 11، دوره 12، شماره 1، فروردین 2024، صفحه 163-174 اصل مقاله (1.26 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9857.658
نویسندگان
Z. Raisi^* ¹؛ J. Zelek²
¹University of Waterloo, Waterloo, Canada and Chabahar Maritime University, Chabahar, Iran.
²Systems Design Engineering Department, University of Waterloo, Canada.
تاریخ دریافت: 05 تیر 1402، تاریخ بازنگری: 14 شهریور 1402، تاریخ پذیرش: 17 شهریور 1402
چکیده
Background and Objectives: Signage is everywhere, and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due to pose, irregular text instances, illumination variations, viewpoint changes, and occlusion factors. Methods: This paper proposes an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. The proposed model leverages a pre-trained Vision Transformer based (ViT) architecture combined with a multi-task transformer-based text detector more suitable for the VPR task. Our central contribution is introducing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. We first equip the ViT backbone using a masked autoencoder (MAE) to capture partially occluded characters to address the occlusion problem. Then, we use a multi-task prediction head for the proposed model to handle arbitrary shapes of text instances with polygon bounding boxes. Results: The evaluation of the proposed architecture's performance for VPR involved conducting several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The well-known evaluation metric, Precision-Recall, was employed to measure the performance of the proposed pipeline. The final model achieved the following performances, Recall = 0.93 and Precision = 0.8, upon testing on this benchmark. Conclusion: The initial experimental results show that the proposed model outperforms the state-of-the-art (SOTA) methods in comparison to the SCTP dataset, which confirms the robustness of the proposed end-to-end scene text detection and recognition model.
کلیدواژه‌ها
Text detection؛ Text Recognition؛ Robotics Localization؛ Deep Learning؛ Visual Place Recognition

مراجع
[1] A. Anoosheh, T. Sattler, R. Timofte, M. Pollefeys, L. Van Gool, “Night-to-day image translation for retrieval-based localization,” in Proc. 2019 International Conference on Robotics and Automation (ICRA): 5958–5964, 2019. [2] R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, J. Sivic, “Netvlad: Cnn architecture for weakly supervised place recognition,” in Proc. IEEE/CVF International Conference on Computer Vision: 5297–5307, 2016. [3] R. Atienza, “Vision transformer for fast and efficient scene text recognition,” Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, pp. 319–334, 2021. [4] J. Baek, G. Kim, J. Lee, S. Park, D. Han, S. Yun, S. J. Oh, H. Lee, “What is wrong with scene text recognition model comparisons? dataset and model analysis,” in Proc. International Conference on Computer Vision (ICCV), 2019. [5] Y. Baek, B. Lee, D. Han, S. Yun, H. Lee, “Character region awareness for text detection,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2019. [6] Y. Baek, S. Shin, J. Baek, S. Park, J. Lee, D. Nam, H. Lee, “Character region attention for text spotting,” ArXiv, vol. abs/2007.09629, 2020. [7] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko, “End-to-end object detection with transformers,” arXiv preprint arXiv:2005.12872, 2020. [8] W. Chan, C. Saharia, G. Hinton, M. Norouzi, N. Jaitly, “Imputer: Sequence modeling via imputation and dynamic programming,” arXiv preprint arXiv:2002.08926, 2020. [9] C. K. Ch’ng, C. S. Chan, “Total-text: A comprehensive dataset for scene text detection and recognition,” in Proc. IAPR International Conference on Document Anal. and Recognition (ICDAR), 1: 935–942, 2017. [10] K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis, A. Mohiuddin, L. Kaiser, et al., “Rethinking attention with performers,” arXiv preprint arXiv:2009.14794, 2020. [11] M. Cummins, P. Newman, “Fab-map: Probabilistic localization and mapping in the space of appearance,” Int. J. Rob. Res., 27(6): 647–665, 2008. [12] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020. [13] S. Fang, H. Xie, Y. Wang, Z. Mao, Y. Zhang, “Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition: 7098–7107, 2021. [14] W. Feng, W. He, F. Yin, X. Y. Zhang, C. L. Liu, “Textdragon: An end-to-end framework for arbitrarily shaped text spotting,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition: 9076–9085, 2019. [15] S. Garg, T. Fischer, M. Milford, “Where is your place, visual place recognition?” arXiv preprint arXiv:2103.06443, 2021. [16] A. Gupta, A. Vedaldi, A. Zisserman, “Synthetic data for text localization in natural images,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition: 2315–2324, 2016. [17] K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, et al., “A survey on the visual transformer,” arXiv preprint arXiv:2012.12556, 2020. [18] K. He, X. Chen, S. Xie, Y. Li, P. Dollar, R. Girshick, “Masked autoencoders are scalable vision learners,” arXiv preprint arXiv:2111.06377, 2021. [19] K. He, G. Gkioxari, P. Dollar, R. Girshick, “Mask R-CNN, ” in Proc. IEEE International Conference on Computer Vision: 2961–2969, 2017. [20] K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 770–778, 2015. [21] S. Hochreiter, J. Schmidhuber, “Long short-term memory,” Neural Comput., 9(8): 1735–1780, 1997. [22] Z. Hong, Y. Petillot, D. Lane, Y. Miao, S. Wang, “Textplace: Visual place recognition and topological localization through reading scene texts,” in Proc. IEEE/CVF International Conference on Computer Vision: 2861–2870, 2019. [23] M. Iwamura, N. Morimoto, K. Tainaka, D. Bazazian, L. Gomez, D. Karatzas, “ICDAR2017 robust reading challenge on omnidirectional video,” in Proc. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 1: 1448–1453, 2017. [24] M. Jaderberg, K. Simonyan, A. Vedaldi, A. Zisserman, “Synthetic data and artificial neural networks for natural scene text recognition, ” arXiv preprint arXiv:1406.2227, 2014. [25] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu, et al., “ICDAR 2015 competition on robust reading,” in Proc. International Conference on Document Analysis and Recognition (ICDAR): 1156–1160, 2015. [26] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. I Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, L. P. De Las Heras, “ICDAR 2013 robust reading competition,” in Proc. International Conference on Document Analysis and Recognition: 1484–1493, 2013. [27] S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, M. Shah, “Transformers in vision: A survey,” arXiv preprint arXiv:2101.01169, 2021. [28] Y. Kittenplon, I. Lavi, S. Fogel, Y. Bar, R. Manmatha, P. Perona, “Towards weakly-supervised text spotting using a multi-task transformer,” arXiv preprint arXiv:2202.05508, 2022. [29] A. B. Laguna, K. Mikolajczyk, “Key. net: Keypoint detection by handcrafted and learned CNN filters revisited,” IEEE Trans. Pattern Anal. Mach. Intell., 45(1): 698-711, 2022. [30] J. Lee, S. Park, J. Baek, S. Joon Oh, S. Kim, H. Lee, “On recognizing texts of arbitrary shapes with 2D self-attention,” in Proc. IEEE CVPR: 546–547, 2020. [31] H. Li, P. Wang, C. Shen, “Towards end-to-end text spotting with convolutional recurrent neural networks,” in Proc. 2017 IEEE International Conference on Computer Vision (ICCV): 5248–5256, 2017. [32] Y. Li, S. Xie, X. Chen, P. Dollar, K. He, R. Girshick, “Bench-marking detection transfer learning with vision transformers,” arXiv preprint arXiv:2111.11429, 2021. [33] M. Liao, G. Pang, J. Huang, T. Hassner, X. Bai, “Mask textspotter v3: Segmentation proposal network for robust scene text spotting,” in Proc. Computer Vision–ECCV 2020: 16th European Conference, Part XI 16: 706–722, 2020. [34] M. Liao, B. Shi, X. Bai, “Textboxes++: A single-shot oriented scene text detector,” IEEE Trans. Image Process., 27(8): 3676–3690, 2018. [35] T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C. L. Zitnick, “Microsoft coco: Common objects in context,” in Proc Eur. Conference on Computer Vision. Springer: 740–755, 2014. [36] V. Nazarzehi, R. Damani, “Decentralised optimal deployment of mobile underwater sensors for covering layers of the ocean,” Indones. J. Electr. Eng. Comput. Sci., 25(2): 840–846, 2022. [37] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg, “SSD: Single shot multibox detector,” in Proc. Eur. Conference on Computer Vision. Springer: 21–37, 2016. [38] X. Liu, D. Liang, S. Yan, D. Chen, Y. Qiao, J. Yan, “FOTS: Fast oriented text spotting with a unified network,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition: 5676–5685, 2018. [39] Y. Liu, H. Chen, C. Shen, T. He, L. Jin, L. Wang, “Abcnet: Real-time scene text spotting with adaptive bezier-curve network,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition: 9809–9818, 2020. [40] Y. Liu, C. Shen, L. Jin, T. He, P. Chen, C. Liu, H. Chen, “Abcnet v2: Adaptive bezier-curve network for real-time end-to-e end text spotting,” arXiv preprint arXiv:2105.03620, 2021. [41] S. Lowry, N. S. Underhauf, P. Newman, J. J. Leonard, D. Cox, P. Corke, M. J. Milford, “Visual place recognition: A survey,” IEEE Trans. Rob., 32(1): 1–19, 2015. [42] S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, R. Young, “ICDAR 2003 robust reading competitions,” in Proc. Seventh Int. Conference on Document Analysis and Recognition: 682– 687, 2023. [43] P. Lyu, M. Liao, C. Yao, W. Wu, X. Bai, “Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes,” in Proc. Eur. Conference on Computer Vision (ECCV) : 67– 83, 2018. [44] C. Masone, B. Caputo, “A survey on deep visual place recognition,” IEEE Access, 9: 19516–19547, 2021. [45] M. J. Milford, G. F. Wyeth, “Seqslam: Visual route-based navigation for sunny summer days and stormy winter nights,” in Proc. IEEE International Conference on Robotics and Automation: 1643–1649, 2012. [46] A. Mishra, K. Alahari, C. V. Jawahar, “Scene text recognition using higher order language priors,” in BMVC, 2012. [47] S. Qin, A. Bissacco, M. Raptis, Y. Fujii, Y. Xiao, “Towards unconstrained end-to-end text spotting,” in Proc. IEEE/CVF International Conference on Computer Vision: 4704–4714, 2019. [48] T. Q. Phan, P. Shivakumara, S. Tian, C. Lim Tan, “Recognizing text with perspective distortion in natural scenes,” in Proc. IEEE International Conference on Computer Vision: 569–576, 2013. [49] Z. Raisi, M. Naiel, P. Fieguth, S. Wardell, J. Zelek, “2d positional embedding-based transformer for scene text recognition,” J. Comput. Vision Imaging Syst., 6(1): 1–4, 2021. [50] Z. Raisi, M. A. Naiel, P. Fieguth, S. Wardell, J. Zelek, “Text detection and recognition in the wild: A review,” arXiv preprint arXiv:2006.04305, 2020. [51] Z. Raisi, M. A. Naiel, G. Younes, S. Wardell, J. Zelek, “2lspe: 2d learnable sinusoidal positional encoding using a transformer for scene text recognition,” in Proc. Conference on Robots and Vision (CRV): 119–126, 2021. [52] Z. Raisi, M. A. Naiel, G. Younes, S. Wardell, J. S. Zelek, “Transformer-based text detection in the wild,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops: 3162–3171, 2021. [53] Z. Raisi, G. Younes, J. Zelek, “Arbitrary shape text detection using transformers,” in Proc. IEEE International Conference on Pattern Recognition (ICPR): 3238-3245, 2022. [54] Z. Raisi, J. Zelek, “Occluded text detection and recognition in the wild,” in IEEE Proceeding Conference on Robots and Vision (CRV): 140-150, 2022. [55] Z. Raisi, J. S. Zelek, “End-to-end scene text spotting at character level,” J. Comput. Vision Imaging Syst., 7(1): 25-27, 2021. [56] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition: 779–788, 2016. [57] S. Ren, K. He, R. Girshick, J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Adv. in Neural Info. Process. Syst.: 91–99, 2015. [58] A. Risnumawan, P. Shivakumara, C. S. Chan, C. L. Tan, “A robust arbitrary text detection system for natural scene images,” Expert Syst. Appl., 41(18): 8027–8048, 2014. [59] D. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning representations by back-propagating errors,” Nature, 323(6088): 533–536, 1986. [60] A. Shahab, F. Shafait, A. Dengel, “ICDAR 2011 robust reading competition challenge 2: Reading text in scene images,” in Proc. International Conference on Doc. Anal. and Recognition: 1491–1496, 2011. [61] B. Shi, M. Yang, X. Wang, P. Lyu, C. Yao, X. Bai, “Aster: An attentional scene text recognizer with flexible rectification,” IEEE Trans. Pattern Anal. Mach. Intell., 41(9): 2035-2048, 2018. [62] Y. Sun, Z. Ni, C.-K. Chng, Y. Liu, C. Luo, C. C. Ng, J. Han, E. Ding, J. Liu, D. Karatzas, et al., “ICDAR 2019 competition on large-scale street view text with partial labeling –RRC-LSVT,” arXiv preprint arXiv:1909.07741, 2019. [63] Y. Tay, M. Dehghani, D. Bahri, D. Metzler, “Efficient transform- ers: A survey,” arXiv preprint arXiv:2009.06732, 2020. [64] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, “Attention is all you need,” in Proc. Advances in Neural Information Processing Systems (NIPS 2017): 5998– 6008, 2017. [65] K. Wang, S. Belongie, “Word spotting in the wild,” in Proc. Eur. Conference on Computer Vision. Springer: 591–604, 2010. [66] C. Yao, X. Bai, W. Liu, Y. Ma, Z. Tu, “Detecting texts of arbitrary orientations in natural images,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition: 1083–1090, 2012. [67] L. Yuliang, J. Lianwen, Z. Shuaitao, Z. Sheng, “Detecting curve text in the wild: New dataset and new solution,” in arXiv preprint arXiv:1712.02170, 2017. [68] X. Zhang, Y. Su, S. Tripathi, Z. Tu, “Text spotting transformers,” arXiv preprint arXiv:2204.01918, 2022. [69] X. Zhang, L. Wang, Y. Su, “Visual place recognition: A survey from deep learning perspective,” Pattern Recognit., 113: 107760, 2021. [70] X. Zhu, W. Su, L. Lu, B. Li, X. Wang, J. Dai, “Deformable detr: Deformable transformers for end-to-end object detection,” arXiv preprint arXiv:2010.04159, 2020. [71] S. X. Zhang, X. Zhu, J. B. Hou, C. Liu, C. Yang, H. Wang, X. C. Yin, “Deep relational reasoning graph network for arbitrary shape text detection,” in Proc. the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 9699-9708, 2020. [72] L. Xing, Z. Tian, W. Huang, M. R. Scott, “Convolutional character networks,” in Proc. the IEEE/CVF International Conference on Computer Vision: 9126-9136, 2019. [73] I. Loshchilov, F. Hutter, “Decoupled weight decay regularization,” in Proc. International Conference on Learning Representations, 2018. [74] G. Liao, Z. Zhu, Y. Bai, T. Liu, Z. Xie, “PSENet-based efficient scene text detection,” EURASIP J. Adv. Signal Process., 97(1), 1-13, 2021. [75] X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou,W. He, J. Liang, “East: an efficient and accurate scene text detector,” in Proc. the IEEE Conference on Computer Vision and Pattern Recognition: 5551-5560, 2017. [76] C. K. Ch'ng, C. S. Chan, “TotalText: A comprehensive dataset for scene text detection and recognition,” in Proc. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 1: 935-942, 2017. [77] L. Yuliang, J. Lianwen, Z. Shuaitao, Z. Sheng, “Detecting curve text in the wild: New dataset and new solution,” in arXiv preprint arXiv:1712.02170, 2017. [78] D. M. Katz, M. J. Bommarito, S. Gao, P. Arredondo, Gpt-4 passes the bar exam. Available at SSRN 4389233. [79] B. M. Lake, T. D. Ullman, J. B. Tenenbaum, S. J. Gershman, “Building machines that learn and think like people,” Behav. Brain sci., 40, 2017.
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Text Detection and Recognition for Robot Localization