[1] [1] Smith R. An overview of the Tesseract OCR engine[C]// Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR 2007), 2007: 629–633.

[2] [2] Mori S, Suen C Y, Yamamoto K. Historical review of OCR research and development[J]. Proc IEEE, 1992, 80(7): 1029–1058.

[3] [3] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[Z]. arXiv:1409.1556, 2014.

[4] [4] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770–778.

[5] [5] Tang Y B, Bu W, Wu X Q. Natural scene text detection based on multi-level MSER[J]. J Zhejiang Univ (Eng Sci), 2016, 50(6): 1134–1140.

[6] [6] Jiang H Y, Zhu L P, Ou Z P. Text recognition of natural scene image based on MSER and Tesseract[J]. Comput Knowl Technol, 2017, 13(33): 213–216.

[7] [7] Zhang K Y, Shao K Y, Lu D. MSER fast skewed scene-text location algorithm[J]. J Harbin Univ Sci Technol, 2019, 24(2): 81–88.

[8] [8] Zhang G H, Huang K, Zhang B, et al. A natural scene text extraction method based on the maximum stable extremal region and stroke width transform[J]. J Xi’an Jiaotong Univ, 2017, 51(1): 135–140.

[9] [9] Nan Y, Bai R L, Li X. Application of convolutional neural network in printed code characters recognition[J]. Opto-Electron Eng, 2015, 42(4): 38–43.

[10] [10] Wang K, Belongie S. Word spotting in the wild[C]//Proceedings of the 11th European Conference on Computer Vision, 2010, 6311: 591–604.

[11] [11] Wang K, Babenko B, Belongie S. End-to-end scene text recognition[ C]//Proceedings of 2011 International Conference on Computer Vision, 2011: 1457–1464.

[12] [12] Tian Z, Huang W L, He T, et al. Detecting text in natural image with connectionist text proposal network[C]//Proceedings of the 14th European Conference on Computer Vision, 2016, 9912: 56–72.

[13] [13] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[ C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.

[14] [14] Liao M H, Shi B G, Bai X, et al. TextBoxes: a fast text detector with a single deep neural network[Z]. arXiv:1611.06779, 2016.

[15] [15] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 21–37.

[16] [16] Tian Z, Huang W L, He T, et al. Detecting text in natural image with connectionist text proposal network[C]//Proceedings of the 14th European Conference on Computer Vision, 2016, 9912: 56–72.

[17] [17] Liao M H, Shi B G, Bai X. TextBoxes++: a single-shot oriented scene text detector[J]. IEEE Trans Image Process, 2018, 27(8): 3676–3690.

[18] [18] Ma J Q, Shao W Y, Ye H, et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE Trans Multimed, 2018, 20(11): 3111–3122.

[19] [19] Zhou X Y, Yao C, Wen H, et al. EAST: an efficient and accurate scene text detector[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 2642–2651.

[20] [20] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 3431–3440.

[21] [21] Deng D, Liu H, Li X, et al. PixelLink: detecting scene text via instance segmentation[Z]. arXiv:1801.01315, 2018.

[22] [22] Shi B G, Bai X, Yao C. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition[J]. I IEEE Trans Pattern Anal Mach Intell, 2017, 39(11): 2298–2304.

[23] [23] Graves A, Fernández S, Gomez F, et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks[C]//Proceedings of the 23rd International Conference on Machine Learning, 2006: 369–376.

[24] [24] Lee C Y, Osindero S. Recursive recurrent nets with attention modeling for OCR in the wild[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 2231–2239.

[25] [25] Sandler M, Howard A, Zhu M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4510–4520.

[26] [26] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 936–944.

[27] [27] Milletari F, Navab N, Ahmadi S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]// Proceedings of the 4th International Conference on 3D Vision (3DV), 2016: 565–571.

[28] [28] Li X, Wang W H, Hou W B, et al. Shape robust text detection with progressive scale expansion network[Z]. arXiv:1806.02559, 2018.

[29] [29] Liu X B, Liang D, Yan S, et al. FOTS: fast oriented text spotting with a unified network[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 5676–5685.

[30] [30] Thakare S, Kamble A, Thengne V, et al. Document Segmentation and Language Translation Using Tesseract-OCR[C]//2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2018.

[31] [31] Shi B G, Yang M K, Wang X G, et al. ASTER: an attentional scene text recognizer with flexible rectification[J]. IEEE Trans Pattern Anal Mach Intell, 2019, 41(9): 2035–2048.