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Scene text detection and recognition with advances in deep learning: a survey

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Abstract

Scene text detection and recognition has become a very active research topic in recent several years. It can find many applications in reality ranging from navigation for vision-impaired people to semantic natural scene understanding. In this survey, we are intended to give a thorough and in-depth reviews on the recent advances on this topic, mainly focusing on the methods that appeared in the past 5 years for text detection and recognition in images and videos, including the recent state-of-the-art techniques on the following three related topics: (1) scene text detection, (2) scene text recognition and (3) end-to-end text recognition system. Compared with the previous survey, this survey pays more attention to the application of deep learning techniques on scene text detection and recognition. We also give a brief introduction of other related works such as script identification, text/non-text classification and text-to-image retrieval. This survey also reviews and summarizes some benchmark datasets that are widely used in the literature. Based on these datasets, performances of state-of-the-art approaches are shown and discussed. Finally, we conclude this survey by pointing out several potential directions on scene text detection and recognition that need to be well explored in the future.

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References

  1. Neumann, L., Matas, J.: A method for text localization and recognition in real-world images. In: Asian Conference on Computer Vision, pp. 770–783. Springer (2010)

  2. Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: 2010 IEEE Conference on CVPR, pp. 2963–2970. IEEE (2010)

  3. Yin, X.C., Yin, X., Huang, K., Hao, H.W.: Robust text detection in natural scene images. IEEE Trans. PAMI 36(5), 970–983 (2014)

    Article  Google Scholar 

  4. Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: 2012 IEEE Conference on CVPR, pp. 3538–3545. IEEE (2012)

  5. Cho, H., Sung, M., Jun, B.: Canny text detector: fast and robust scene text localization algorithm. In: CVPR, pp. 3566–3573 (2016)

  6. Busta, M., Neumann, L., Matas, J.: Fastext: efficient unconstrained scene text detector. In: ICCV, pp. 1206–1214 (2015)

  7. Zhong, Y., Zhang, H., Jain, A.K.: Automatic caption localization in compressed video. IEEE Trans. PAMI 22(4), 385–392 (2000)

    Article  Google Scholar 

  8. Hanif, S.M., Prevost, L., Negri, P.: A cascade detector for text detection in natural scene images. In: ICPR, pp. 1–4 (2008)

  9. Hanif, S.M., Prevost, L.: Text detection and localization in complex scene images using constrained adaboost algorithm. In: ICDAR’09, pp. 1–5. IEEE (2009)

  10. Zhang, Z., Shen, W., Yao, C., Bai, X.: Symmetry-based text line detection in natural scenes. In: CVPR, pp. 2558–2567 (2015)

  11. Liang, G., Shivakumara, P., Lu, T., Tan, C.L.: A new wavelet-laplacian method for arbitrarily-oriented character segmentation in video text lines. In: ICDAR’15, pp. 926–930. IEEE (2015)

  12. Huang, W., Qiao, Y., Tang, X.: Robust scene text detection with convolution neural network induced mser trees. In: ECCV, pp. 497–511. Springer (2014)

  13. Zhong, Z., Sun, L., Huo, Q.: Improved localization accuracy by locnet for faster r-cnn based text detection. In: DICDAR’17, vol. 1, pp. 923–928. IEEE (2017)

  14. Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., Bai, X.: Multi-oriented text detection with fully convolutional networks. In: CVPR, pp. 4159–4167 (2016)

  15. Zhu, S., Zanibbi, R.: A text detection system for natural scenes with convolutional feature learning and cascaded classification. In: CVPR, pp. 625–632 (2016)

  16. Qin, S., Manduchi, R.: Cascaded segmentation-detection networks for word-level text spotting. arXiv preprint arXiv:1704.00834 (2017)

  17. Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: CVPR, pp. 2315–2324 (2016)

  18. Tang, Y., Wu, X.: Scene text detection and segmentation based on cascaded convolution neural networks. IEEE Trans. Image Process. 26(3), 1509–1520 (2017)

    Article  MATH  Google Scholar 

  19. Wang, C., Yin, F., Liu, C.L.: Scene text detection with novel superpixel based character candidate extraction. In: ICDAR’17, vol. 1, pp. 929–934. IEEE (2017)

  20. Turki, H., Halima, M.B., Alimi, A.M.: Text detection based on mser and cnn features. In: ICDAR’17, vol. 1, pp. 949–954. IEEE (2017)

  21. Tian, S., Pan, Y., Huang, C., Lu, S., Yu, K., Lim Tan, C.: Text flow: a unified text detection system in natural scene images. In: ICCV, pp. 4651–4659 (2015)

  22. Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: ECCV, pp. 56–72. Springer (2016)

  23. He, T., Huang, W., Qiao, Y., Yao, J.: Text-attentional convolutional neural network for scene text detection. IEEE Trans. Image Process. 25(6), 2529–2541 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  24. Fabrizio, J., Robert-Seidowsky, M., Dubuisson, S., Calarasanu, S., Boissel, R.: Textcatcher: a method to detect curved and challenging text in natural scenes. IJDAR 19(2), 99–117 (2016)

    Article  Google Scholar 

  25. Pei, W.Y., Yang, C., Kau, L.J., Yin, X.C.: Multi-orientation scene text detection with multi-information fusion. In: ICPR, pp. 657–662. IEEE (2016)

  26. Yin, X.C., Pei, W.Y., Zhang, J., Hao, H.W.: Multi-orientation scene text detection with adaptive clustering. IEEE Trans. PAMI 37(9), 1930–1937 (2015)

    Article  Google Scholar 

  27. Kang, L., Li, Y., Doermann, D.: Orientation robust text line detection in natural images. In: CVPR, pp. 4034–4041 (2014)

  28. Gomez, L., Karatzas, D.: Textproposals: a text-specific selective search algorithm for word spotting in the wild. Pattern Recognit. 70, 60–74 (2017)

    Article  Google Scholar 

  29. Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: East: an efficient and accurate scene text detector. arXiv preprint arXiv:1704.03155 (2017)

  30. Shi, B., Bai, X., Belongie, S.: Detecting oriented text in natural images by linking segments. In: CVPR, vol. 3 (2017)

  31. Liu, Y., Jin, L.: Deep matching prior network: toward tighter multi-oriented text detection. In: CVPR, vol. 2, p. 8 (2017)

  32. Sheshadri, K., Divvala, S.K.: Exemplar driven character recognition in the wild. In: BMVC, pp. 1–10 (2012)

  33. Shi, C., Wang, C., Xiao, B., Zhang, Y., Gao, S., Zhang, Z.: Scene text recognition using part-based tree-structured character detection. In: CVPR, pp. 2961–2968. IEEE (2013)

  34. Coates, A., Carpenter, B., Case, C., Satheesh, S., Suresh, B., Wang, T., Wu, D.J., Ng, A.Y.: Text detection and character recognition in scene images with unsupervised feature learning. In: ICDAR’11, pp. 440–445. IEEE (2011)

  35. Yao, C., Bai, X., Shi, B., Liu, W.: Strokelets: a learned multi-scale representation for scene text recognition. In: CVPR, pp. 4042–4049 (2014)

  36. Lee, C.Y., Bhardwaj, A., Di, W., Jagadeesh, V., Piramuthu, R.: Region-based discriminative feature pooling for scene text recognition. In: CVPR, pp. 4050–4057 (2014)

  37. Lou, X., Kansky, K., Lehrach, W., Laan, C., Marthi, B., Phoenix, D., George, D.: Generative shape models: joint text recognition and segmentation with very little training data. In: NIPS, pp. 2793–2801 (2016)

  38. Liang, G., Shivakumara, P., Lu, T., Tan, C.L.: Multi-spectral fusion based approach for arbitrarily oriented scene text detection in video images. IEEE Trans. Image Process. 24(11), 4488–4501 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  39. Elagouni, K., Garcia, C., Mamalet, F., Sébillot, P.: Combining multi-scale character recognition and linguistic knowledge for natural scene text OCR. In: 2012 10th IAPR International Workshop on Document Analysis Systems (DAS), pp. 120–124. IEEE (2012)

  40. Phan, T.Q., Shivakumara, P., Tian, S., Tan, C.L.: Recognizing text with perspective distortion in natural scenes. In: ICCV, pp. 569–576. IEEE (2013)

  41. Weinman, J.J., Butler, Z., Knoll, D., Feild, J.: Toward integrated scene text reading. IEEE Trans. PAMI 36(2), 375–387 (2014)

    Article  Google Scholar 

  42. Su, B., Lu, S.: Accurate scene text recognition based on recurrent neural network. In: ACCV, pp. 35–48. Springer (2014)

  43. Ghosh, S.K., Valveny, E., Bagdanov, A.D.: Visual attention models for scene text recognition. arXiv preprint arXiv:1706.01487 (2017)

  44. Shi, B., Wang, X., Lyu, P., Yao, C., Bai, X.: Robust scene text recognition with automatic rectification. In: CVPR, pp. 4168–4176 (2016)

  45. Lee, C.Y., Osindero, S.: Recursive recurrent nets with attention modeling for OCR in the wild. In: CVPR, pp. 2231–2239 (2016)

  46. He, P., Huang, W., Qiao, Y., Loy, C.C., Tang, X.: Reading scene text in deep convolutional sequences. AAAI 16, 3501–3508 (2016)

    Google Scholar 

  47. Yang, X., He, D., Zhou, Z., Kifer, D., Giles, C.L.: Learning to read irregular text with attention mechanisms. In: IJCAI, pp. 3280–3286 (2017)

  48. 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. PAMI 39(11), 2298–2304 (2017)

    Article  Google Scholar 

  49. Neumann, L., Matas, J.: Scene text localization and recognition with oriented stroke detection. In: ICCV, pp. 97–104 (2013)

  50. Jaderberg, M., Vedaldi, A., Zisserman, A.: Deep features for text spotting. In: ECCV, pp. 512–528. Springer (2014)

  51. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Reading text in the wild with convolutional neural networks. IJCV 116(1), 1–20 (2016)

    Article  MathSciNet  Google Scholar 

  52. Neumann, L., Matas, J.: Efficient scene text localization and recognition with local character refinement. In: ICDAR’15, pp. 746–750. IEEE (2015)

  53. Neumann, L., Matas, J.: Real-time lexicon-free scene text localization and recognition. IEEE Trans. PAMI 38(9), 1872–1885 (2016)

    Article  Google Scholar 

  54. Yao, C., Bai, X., Liu, W.: A unified framework for multioriented text detection and recognition. IEEE Trans. Image Process. 23(11), 4737–4749 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  55. Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: Textboxes: a fast text detector with a single deep neural network. In: AAAI, pp. 4161–4167 (2017)

  56. Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. PAMI 37(7), 1480–1500 (2015)

    Article  Google Scholar 

  57. Zhu, Y., Yao, C., Bai, X.: Scene text detection and recognition: recent advances and future trends. Front. Comput. Sci. 10(1), 19–36 (2016)

    Article  Google Scholar 

  58. Yin, X.C., Zuo, Z.Y., Tian, S., Liu, C.L.: Text detection, tracking and recognition in video: a comprehensive survey. IEEE Trans. Image Process. 25(6), 2752–2773 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  59. Weinman, J.J.: Unified Detection and Recognition for Reading Text in Scene Images. University of Massachusetts Amherst, Amherst (2008)

    Google Scholar 

  60. Field, J.: Improving text recognition in images of natural scenes. PhD thesis, University of Massachusetts Amherst (2014)

  61. Jaderberg, M.: Deep learning for text spotting. PhD thesis (2015)

  62. Mishra, A.: Understanding Text in Scene Images. PhD thesis, International Institute of Information Technology Hyderabad (2016)

  63. Bissacco, A., Cummins, M., Netzer, Y., Neven, H.: Photoocr: Reading text in uncontrolled conditions. In: ICCV, pp. 785–792. IEEE (2013)

  64. Pan, Y.F., Hou, X., Liu, C.L.: Text localization in natural scene images based on conditional random field. In: ICDAR’09, pp. 6–10. IEEE (2009)

  65. Pan, Y.F., Hou, X., Liu, C.L.: A hybrid approach to detect and localize texts in natural scene images. IEEE Trans. Image Process. 20(3), 800–813 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  66. Wang, Y., Shi, C., Xiao, B., Wang, C.: Mrf based text binarization in complex images using stroke feature. In: ICDAR’15, pp. 821–825. IEEE (2015)

  67. Koo, H.I., Cho, N.I.: Text-line extraction in handwritten chinese documents based on an energy minimization framework. IEEE Trans. Image Process. 21(3), 1169–1175 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  68. Mishra, A., Alahari, K., Jawahar, C.: Top-down and bottom-up cues for scene text recognition. In: CVPR, pp. 2687–2694. IEEE (2012)

  69. Sharma, N., Mandal, R., Sharma, R., Roy, P.P., Pal, U., Blumenstein, M.: Multi-lingual text recognition from video frames. In: ICDAR’15, pp. 951–955. IEEE (2015)

  70. Canny, J.: A computational approach to edge detection. IEEE Trans. PAMI 8, 679–698 (1986)

    Article  Google Scholar 

  71. Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybern. 61(2), 103–113 (1989)

    Article  Google Scholar 

  72. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. PAMI 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  73. Van Loan, C.: Computational Frameworks for the Fast Fourier Transform. SIAM, Philadelphia (1992)

    Book  MATH  Google Scholar 

  74. Jung, K., Kim, K.I., Jain, A.K.: Text information extraction in images and video: a survey. Pattern Recognit. 37(5), 977–997 (2004)

    Article  Google Scholar 

  75. Zuo, Z.Y., Tian, S., Pei, W.Y., Yin, X.C.: Multi-strategy tracking based text detection in scene videos. In: ICDAR’15, pp. 66–70. IEEE (2015)

  76. Tian, S., Yin, X.C., Su, Y., Hao, H.W.: A unified framework for tracking based text detection and recognition from web videos. IEEE Trans. PAMI 40(3), 542–554 (2018)

    Article  Google Scholar 

  77. Shivakumara, P., Phan, T.Q., Tan, C.L.: A laplacian approach to multi-oriented text detection in video. IEEE Trans. PAMI 33(2), 412–419 (2011)

    Article  Google Scholar 

  78. Yousfi, S., Berrani, S.A., Garcia, C.: Deep learning and recurrent connectionist-based approaches for arabic text recognition in videos. In: ICDAR’15, pp. 1026–1030. IEEE (2015)

  79. Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: CVPR, pp. 1083–1090. IEEE (2012)

  80. Nicolaou, A., Bagdanov, A.D., Gómez, L., Karatzas, D.: Visual script and language identification. In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 393–398. IEEE (2016)

  81. Shi, B., Bai, X., Yao, C.: Script identification in the wild via discriminative convolutional neural network. Pattern Recognit. 52, 448–458 (2016)

    Article  Google Scholar 

  82. Gomez, L., Nicolaou, A., Karatzas, D.: Improving patch-based scene text script identification with ensembles of conjoined networks. Pattern Recognit. 67, 85–96 (2017)

    Article  Google Scholar 

  83. Sharma, N., Mandal, R., Sharma, R., Pal, U., Blumenstein, M.: ICDAR 2015 competition on video script identification (cvsi 2015). In: ICDAR’15, pp. 1196–1200. IEEE (2015)

  84. Delaye, A., Liu, C.L.: Contextual text/non-text stroke classification in online handwritten notes with conditional random fields. Pattern Recognit. 47(3), 959–968 (2014)

    Article  Google Scholar 

  85. Van Phan, T., Nakagawa, M.: Text/non-text classification in online handwritten documents with recurrent neural networks. In: ICFHR, pp. 23–28. IEEE (2014)

  86. Sharma, N., Shivakumara, P., Pal, U., Blumenstein, M., Tan, C.L.: Piece-wise linearity based method for text frame classification in video. Pattern Recognit. 48(3), 862–881 (2015)

    Article  Google Scholar 

  87. Bai, X., Shi, B., Zhang, C., Cai, X., Qi, L.: Text/non-text image classification in the wild with convolutional neural networks. Pattern Recognit. 66, 437–446 (2017)

    Article  Google Scholar 

  88. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  89. Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recognit. 33(2), 225–236 (2000)

    Article  Google Scholar 

  90. Yi, C., Tian, Y.: Localizing text in scene images by boundary clustering, stroke segmentation, and string fragment classification. IEEE Trans. Image Process. 21(9), 4256–4268 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  91. Howe, N.R.: Document binarization with automatic parameter tuning. IJDAR 16(3), 247–258 (2013)

    Article  Google Scholar 

  92. Zhang, Z., Wang, W.: A novel approach for binarization of overlay text. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4259–4264. IEEE (2013)

  93. Tensmeyer, C., Martinez, T.: Document image binarization with fully convolutional neural networks. arXiv preprint arXiv:1708.03276 (2017)

  94. Peng, X., Cao, H., Natarajan, P.: Using convolutional encoder–decoder for document image binarization. In: ICDAR’17, vol. 1, pp. 708–713. IEEE (2017)

  95. Meng, G., Yuan, K., Wu, Y., Xiang, S., Pan, C.: Deep networks for degraded document image binarization through pyramid reconstruction. In: ICDAR’17, vol. 1, pp. 727–732. IEEE (2017)

  96. Ha, J.W., Lee, B.J., Zhang, B.T.: Text-to-image retrieval based on incremental association via multimodal hypernetworks. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3245–3250. IEEE (2012)

  97. Mishra, A., Alahari, K., Jawahar, C.: Image retrieval using textual cues. In: ICCV, pp. 3040–3047. IEEE (2013)

  98. Karaoglu, S., Tao, R., Gevers, T., Smeulders, A.W.M.: Words matter: scene text for image classification and retrieval. IEEE Trans. Multimed. 19(5), 1063–1076 (2017)

    Article  Google Scholar 

  99. Rong, X., Yi, C., Tian, Y.: Unambiguous text localization and retrieval for cluttered scenes. In: CVPR, pp. 3279–3287. IEEE (2017)

  100. Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 robust reading competitions. In: ICDAR’03, pp. 682–687. IEEE (2003)

  101. Lucas, SM.: ICDAR 2005 text locating competition results. In: ICDAR’05, pp. 80–84. IEEE (2005)

  102. Shahab, A., Shafait, F., Dengel, A.: ICDAR 2011 robust reading competition challenge 2: reading text in scene images. In: ICDAR’11, pp. 1491–1496. IEEE (2011)

  103. Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., i Bigorda, L.G., Mestre, S.R., Mas, J., Mota, D.F., Almazan, J.A., de las Heras, L.P.: ICDAR 2013 robust reading competition. In: ICDAR’13, pp. 1484–1493. IEEE (2013)

  104. Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V.R., Lu, S., et al.: ICDAR 2015 competition on robust reading. In: ICDAR’15, pp. 1156–1160. IEEE (2015)

  105. Veit, A., Matera, T., Neumann, L., Matas, J., Belongie, S.: Coco-text: Dataset and benchmark for text detection and recognition in natural images. arXiv preprint arXiv:1601.07140 (2016)

  106. Mishra, A., Alahari, K., Jawahar, C.: Scene text recognition using higher order language priors. In: BMVC, BMVA (2012)

  107. Campos, T.E.D., Babu, B.R., Varma, A.M.: Character Recognition in Natural Images. Chapman & Hall, Boca Raton (2009)

    Google Scholar 

  108. SeongHun, L., Min Su, C., Kyomin, J., Jin Hyung, K.: Scene text extraction with edge constraint and text collinearity. In: 2010 20th International Conference on Pattern Recognition, pp. 3983–3986. IEEE (2010)

  109. Yi, C., Tian, Y.: Text string detection from natural scenes by structure-based partition and grouping. IEEE Trans. Image Process. 20(9), 2594–2605 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  110. Ch’ng, C.K., Chan, C.S.: Total-text: a comprehensive dataset for scene text detection and recognition. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 935–942. IEEE (2017)

  111. Shi, B., Yao, C., Liao, M., Yang, M., Xu, P., Cui, L., Belongie, S., Lu, S., Bai, X.: ICDAR 2017 competition on reading chinese text in the wild (rctw-17). arXiv preprint arXiv:1708.09585 (2017)

  112. Risnumawan, A., Shivakumara, P., Chan, C.S., Tan, C.L.: A robust arbitrary text detection system for natural scene images. Expert Syst. Appl. 41(18), 8027–8048 (2014)

    Article  Google Scholar 

  113. Wolf, C., Jolion, J.M.: Object count/area graphs for the evaluation of object detection and segmentation algorithms. IJDAR 8(4), 280–296 (2006)

    Article  Google Scholar 

  114. Cheng, Z., Bai, F., Xu, Y., Zheng, G., Pu, S., Zhou, S.L Focusing attention: towards accurate text recognition in natural images. In: ICCV, pp. 5086–5094. IEEE (2017)

  115. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Deep structured output learning for unconstrained text recognition. In: ICLR (2015)

  116. Alsharif, O., Pineau, J.: End-to-end text recognition with hybrid hmm maxout models. arXiv preprint arXiv:1310.1811 (2013)

  117. Wang, K., Babenko, B., Belongie, S.: End-to-end scene text recognition. In: ICCV, pp. 1457–1464. IEEE (2011)

  118. Li, H., Wang, P., Shen, C.: Towards end-to-end text spotting with convolutional recurrent neural networks. In: Proc. ICCV, pp. 5238–5246 (2017)

  119. Hu, H., Zhang, C., Luo, Y., Wang, Y., Han, J., Ding, E.: Wordsup: exploiting word annotations for character based text detection. In: ICCV (2017)

  120. He, W., Zhang, X.Y., Yin, F., Liu, C.L.: Deep direct regression for multi-oriented scene text detection. arXiv preprint arXiv:1703.08289 (2017)

  121. He, P., Huang, W., He, T., Zhu, Q., Qiao, Y., Li, X.: Single shot text detector with regional attention. In: ICCV (2017)

  122. Busta, M., Neumann, L., Matas, J.: Deep textspotter: an end-to-end trainable scene text localization and recognition framework. In: ICCV, pp. 22–29 (2017)

  123. Wu, Y., Natarajan, P.: Self-organized text detection with minimal post-processing via border learning. In: CVPR, pp. 5000–5009 (2017)

  124. Gordo, A.: Supervised mid-level features for word image representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2956–2964 (2015)

  125. Almazan, J., Gordo, A., Fornes, A., Valveny, E.: Word spotting and recognition with embedded attributes. IEEE Trans. PAMI 36(12), 2552–2566 (2014)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61370039, and the Beijing Natural Science Foundation under Grant L172053.

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  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Xiyan Liu, Gaofeng Meng & Chunhong Pan

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Xiyan Liu

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Liu, X., Meng, G. & Pan, C. Scene text detection and recognition with advances in deep learning: a survey. IJDAR 22, 143–162 (2019). https://doi.org/10.1007/s10032-019-00320-5

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