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Artificial Intelligence Review

Erschienen in:

01.01.2021

Deep learning approaches to scene text detection: a comprehensive review

verfasst von: Tauseef Khan, Ram Sarkar, Ayatullah Faruk Mollah

Erschienen in: Artificial Intelligence Review | Ausgabe 5/2021

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Abstract

In recent times, text detection in the wild has significantly raised its ability due to tremendous success of deep learning models. Applications of computer vision have emerged and got reshaped in a new way in this booming era of deep learning. In the last decade, research community has witnessed drastic changes in the area of text detection from natural scene images in terms of approach, coverage and performance due to huge advancement of deep neural network based models. In this paper, we present (1) a comprehensive review of deep learning approaches towards scene text detection, (2) suitable deep frameworks for this task followed by critical analysis, (3) a categorical study of publicly available scene image datasets and applicable standard evaluation protocols with their pros and cons, and (4) comparative results and analysis of reported methods. Moreover, based on this review and analysis, we precisely mention possible future scopes and thrust areas of deep learning approaches towards text detection from natural scene images on which upcoming researchers may focus.

Vorheriger Artikel A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications

Nächster Artikel Machine learning towards intelligent systems: applications, challenges, and opportunities

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Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. In: arXiv:1603.04467

Ansari GJ, Shah JH, Yasmin M, Sharif M, Fernandes SL (2018) A novel machine learning approach for scene text extraction. Future Gener Comput Syst 87:328–340CrossRef

Baek Y, Lee B, Han D, Yun S, Lee H (2019) Character region awareness for text detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9365–9374

Bagri N, Johari PK (2015) A comparative study on feature extraction using texture and shape for content based image retrieval. Int J Adv Sci Technol 80(4):41–52CrossRef

Bai B, Yin F, Liu CL (2013) Scene text localization using gradient local correlation. In: 12th international conference on document analysis and recognition, pp 1380–1384

Bastien F, Lamblin P, Pascanu R, Bergstra J, Goodfellow I, Bergeron A, Bouchard N, Warde-Farley D, Bengio Y (2012) Theano: new features and speed improvements. In: arXiv:1211.5590

Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: the CLEAR MOT metrics. J Image Video Process 1

Busta M, Neumann L, Matas J (2017) Deep textspotter: an end-to-end trainable scene text localization and recognition framework. In: Proceedings of the IEEE international conference on computer vision, pp 2204–2212

Ch’ng CK, Chan CS (2017) Total-text: a comprehensive dataset for scene text detection and recognition. In: 14th international conference on document analysis and recognition, pp 935–942

Ch’ng CK, Chan CS, Liu CL (2019) Total-text: toward orientation robustness in scene text detection. In: International journal on document analysis and recognition, pp 1–22 (In press)

Chen X, Yuille AL (2004) Detecting and reading text in natural scenes. In: IEEE conference on computer vision and pattern recognition, vol 2, pp II–II

Chen H, Tsai SS, Schroth G, Chen DM, Grzeszczuk R, Girod B (2011) Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 18th IEEE international conference on image processing, pp 2609–2612

Cho H, Sung M, Jun B (2016) Canny text detector: fast and robust scene text localization algorithm. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3566–3573

CIFAR-10 Dataset. https://www.cs.toronto.edu/~kriz/cifar.html. Accessed on 14 June 2020

Coates A, Carpenter B, Case C, Satheesh S, Suresh B, Wang T, Wu DJ, Ng AY (2011) Text detection and character recognition in scene images with unsupervised feature learning. In: IEEE international conference on document analysis and recognition, pp 440–445

da Silveira TL, Kozakevicius AJ, Rodrigues CR (2017) Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain. Med Biol Eng Comput 55(2):343–352CrossRef

Dai Y, Huang Z, Gao Y, Xu Y, Chen K, Guo J, Qiu W (2018) Fused text segmentation networks for multi-oriented scene text detection. In: 24th international conference on pattern recognition, pp 3604–3609

Deng D, Liu H, Li X, Cai D (2018) Pixellink: detecting scene text via instance segmentation. In: 32th international conference of atrificial intelligence AAAI, pp 6773–6780

Dey S, Shivakumara P, Raghunandan KS, Pal U, Lu T, Kumar GH, Chan CS (2017) Script independent approach for multi-oriented text detection in scene image. Neurocomputing 242:96–112CrossRef

Epshtein B, Ofek E, Wexler Y (2010) Detecting text in natural scenes with stroke width transform. In: IEEE computer society conference on computer vision and pattern recognition, pp 2963–2970

Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136CrossRef

Fathi A, Wojna Z, Rathod V, Wang P, Song HO, Guadarrama S, Murphy KP (2017) Semantic instance segmentation via deep metric learning. In: arXiv:1703.10277

Feng W, He W, Yin F, Zhang XY, Liu CL (2019) TextDragon: an end-to-end framework for arbitrary shaped text spotting. In: Proceedings of the IEEE international conference on computer vision, pp 9076–9085

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

Francis LM, Sreenath N (2017) TEDLESS–Text detection using least-square SVM from natural scene. J King Saud Univ Comput Inf Sci 29(4)

Fu CY, Liu W, Ranga A, Tyagi A, Berg AC (2017) DSSD: deconvolutional single shot detector. In: arXiv:1701.06659

Gao J, Wang Q, Yuan Y (2019) Convolutional regression network for multi-oriented text detection. IEEE Access 7:96424–96433CrossRef

Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

Gllavata J, Ewerth R, Freisleben B (2004) Text detection in images based on unsupervised classification of high-frequency wavelet coefficients. In: 17th international conference on pattern recognition, vol 1, pp 425–428

Google Street View. http://maps.google.com

Greenhalgh J, Mirmehdi M (2012) Real-time detection and recognition of road traffic signs. IEEE Trans Intell Transp Syst 13(4):1498–1506CrossRef

Gupta A, Vedaldi A, Zisserman A (2016) Synthetic data for text localisation in natural images. In: IEEE conference on computer vision and pattern recognition, pp 2315–2324

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

He K, Zhang X, Ren S, Sun J (2016b) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

He D, Yang X, Liang C, Zhou Z, Ororbi AG, Kifer D, Lee Giles C (2017a) Multi-scale FCN with cascaded instance aware segmentation for arbitrary oriented word spotting in the wild. In: IEEE conference on computer vision and pattern recognition, pp 3519–3528

He P, Huang W, He T, Zhu Q, Qiao Y, Li X (2017b) Single shot text detector with regional attention. In: IEEE international conference on computer vision, pp 3047–3055

He W, Zhang XY, Yin F, Liu CL (2017c) Deep direct regression for multi-oriented scene text detection. In: IEEE international conference on computer vision, pp 745–753

He K, Gkioxari G, Dollár P, Girshick R (2017d) Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969

He T, Tian Z, Huang W, Shen C, Qiao Y, Sun C (2018a) An end-to-end textspotter with explicit alignment and attention. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5020–5029

He W, Zhang XY, Yin F, Liu CL (2018b) Multi-oriented and multi-lingual scene text detection with direct regression. IEEE Trans Image Process 27(11):5406–5419MathSciNetCrossRef

He W, Zhang XY, Yin F, Luo Z, Ogier JM, Liu CL (2020) Realtime multi-scale scene text detection with scale-based region proposal network. Pattern Recognit 98:107026CrossRef

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef

Huang X (2019) Automatic video scene text detection based on saliency edge map. Multimed Tools Appl 78(24):34819–34838CrossRef

Huang W, Lin Z, Yang J, Wang J (2013) Text localization in natural images using stroke feature transform and text covariance descriptors. In: IEEE international conference on computer vision, pp 1241–1248

Huang W, Qiao Y, Tang X (2014) Robust scene text detection with convolution neural network induced mser trees. In: European conference on computer vision, pp 497–511

Huang L, Yang Y, Deng Y, Yu Y (2015) Densebox: unifying landmark localization with end to end object detection. In: arXiv:1509.04874

Huang Z, Zhong Z, Sun L, Huo Q (2019) Mask R-CNN with pyramid attention network for scene text detection. In: 2019 IEEE winter conference on applications of computer vision, pp 764–772

Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2016) Reading text in the wild with convolutional neural networks. Int J Comput Vis 116(1):1–20MathSciNetCrossRef

Jeon M, Jeong YS (2020) Compact and accurate scene text detector. Appl Sci 10(6):2096CrossRef

Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: 22nd international conference on multimedia, pp 675–678

Jiang Y, Zhu X, Wang X, Yang S, Li W, Wang H, Fu P, Luo Z (2017) R²CNN: rotational region CNN for orientation robust scene text detection. In: arXiv:1706.09579

Jiang M, Cheng J, Chen M, Ku X (2018) An improved text localization method for natural scene images. J Phys 960(1):012027

Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R (2019) A survey of deep learning-based object detection. IEEE Access 7:128837–128868CrossRef

Joan SF, Valli S (2019) A survey on text information extraction from born-digital and scene text images. Proc Natl Acad Sci India Sect A 89(1):77–101CrossRef

Karatzas D, Shafait F, Uchida S, Iwamura M, Bigorda LG, Mestre SR, Mas J, Mota DF, Almazan JA, De Las Heras LP (2011) ICDAR 2011 robust reading competition. In: 12th international conference on document analysis and recognition, pp 1484–1493

Karatzas D, Shafait F, Uchida S, Iwamura M, Bigorda LG, Mestre SR, Mas J, Mota DF, Almazan JA, De Las Heras LP (2013) ICDAR 2013 robust reading competition. In: 12th international conference on document analysis and recognition, pp 1484–1493

Karatzas D, Gomez-Bigorda L, Nicolaou A, Ghosh S, Bagdanov A, Iwamura M, Matas J, Neumann L, Chandrasekhar VR, Lu S, Shafait F (2015) ICDAR 2015 competition on robust reading. In: 13th international conference on document analysis and recognition, pp 1156–1160

Kasturi R, Goldgof D, Soundararajan P, Manohar V, Garofolo J, Bowers R, Boonstra M, Korzhova V, Zhang J (2008) Framework for performance evaluation of face, text, and vehicle detection and tracking in video: data, metrics, and protocol. IEEE Trans Pattern Anal Mach Intell 31(2):319–336CrossRef

Ketkar N (2017) Introduction to keras. In: Deep learning with python, pp 97–111

Khan T, Mollah AF (2019a) Distance transform-based stroke feature descriptor for text non-text classification. In: Recent developments in machine learning and data analytics, pp 189–200

Khan T, Mollah AF (2019b) AUTNT-A component level dataset for text non-text classification and benchmarking with novel script invariant feature descriptors and D-CNN. Multimed Tools Appl 78(22):32159–32186CrossRef

Khan FA, Tahir MA, Khelifi F, Bouridane A, Almotaeryi R (2017) Robust off-line text independent writer identification using bagged discrete cosine transform features. Expert Syst Appl 71:404–415CrossRef

Kim KH, Hong S, Roh B, Cheon Y, Park M (2016) Pvanet: deep but lightweight neural networks for real-time object detection. In: arXiv:1608.08021

Kobchaisawat T, Chalidabhongse TH, Satoh SI (2020) Scene text detection with polygon offsetting and border augmentation. Electronics 9(1):117CrossRef

Kong S, Fowlkes CC (2018) Recurrent pixel embedding for instance grouping. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9018–9028

Koo HI, Kim DH (2013) Scene text detection via connected component clustering and nontext filtering. IEEE Trans Image Process 22(6):2296–2305MathSciNetMATHCrossRef

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. IEEE 86(11):2278–2324CrossRef

Lee S, Cho MS, Jung K, Kim JH (2010) Scene text extraction with edge constraint and text collinearity. In: 20th international conference on pattern recognition, pp 3983–3986

Lee JJ, Lee PH, Lee SW, Yuille A, Koch C (2011a) Adaboost for text detection in natural scene. In: 2011 International conference on document analysis and recognition, pp 429–434

Lee JJ, Lee PH, Lee SW, Yuille A, Koch C (2011b) Adaboost for text detection in natural scene. In: International conference on document analysis and recognition, pp 429–434

Lee CY, Baek Y, Lee H (2019) TedEval: a fair evaluation metric for scene text detectors. In: arXiv:1907.01227

Leibe B, Matas J, Sebe N, Welling M (eds) (2016) Computer vision—ECCV 2016. In: 14th European conference, vol 9908

Li Y, Lu H (2012) Scene text detection via stroke width. In: 21st international conference on pattern recognition, pp 681–684

Li H, Wang P, Shen C (2017) Towards end-to-end text spotting with convolutional recurrent neural networks. In: Proceedings of the IEEE international conference on computer vision, pp 5238–5246

Li X, Wang W, Hou W, Liu RZ, Lu T, Yang J (2018) Shape robust text detection with progressive scale expansion network. In: arXiv:1806.02559

Liang J, Phillips IT, Haralick RM (1997) Performance evaluation of document layout analysis algorithms on the UW data set. Int Soc Opt Photonics Doc Recognit 3027:149–160

Liang G, Shivakumara P, Lu T, Tan CL (2015) A new wavelet-Laplacian method for arbitrarily-oriented character segmentation in video text lines. In: 13th international conference on document analysis and recognition, pp 926–930

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

Liao M, Shi B, Bai X (2018a) Textboxes++: a single-shot oriented scene text detector. IEEE Trans Image Process 27(8):3676–3690MathSciNetMATHCrossRef

Liao M, Zhu Z, Shi B, Xia GS, Bai X (2018b) Rotation-sensitive regression for oriented scene text detection. In: IEEE conference on computer vision and pattern recognition, pp 5909–5918

Liao M, Lyu P, He M, Yao C, Wu W, Bai X (2019a) Mask textspotter: an end-to-end trainable neural network for spotting text with arbitrary shapes. In: IEEE transactions on pattern analysis and machine intelligence. https://doi.org/10.1109/tpami.2019.2937086

Liao M, Wan Z, Yao C, Chen K, Bai X (2019b) Real-time scene text detection with differentiable binarization. In: arXiv:1911.08947

Liao M, Song B, Long S, He M, Yao C, Bai X (2020) SynthText3D: synthesizing scene text images from 3D virtual worlds. Sci China Inf Sci 63(2):120105CrossRef

Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision, pp 740–755

Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition, pp 2117–2125

Lin H, Yang P, Zhang F (2019) Review of scene text detection and recognition. In: Archives of computational methods in engineering, pp 1–22

Liu Y, Jin L (2017) Deep matching prior network: toward tighter multi-oriented text detection. In: IEEE international conference on computer vision and pattern recognition, pp 3454–3461

Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016a) SSD: single shot multibox detector. In: European conference on computer vision, pp 21–37

Liu L, Lao S, Fieguth PW, Guo Y, Wang X, Pietikäinen M (2016b) Median robust extended local binary pattern for texture classification. IEEE Trans Image Process 25(3):1368–1381MathSciNetMATHCrossRef

Liu L, Fieguth P, Guo Y, Wang X, Pietikäinen M (2017) Local binary features for texture classification: taxonomy and experimental study. Pattern Recognit 62:135–160CrossRef

Liu Z, Lin G, Yang S, Feng J, Lin W, Goh WL (2018a) Learning markov clustering networks for scene text detection. In: IEEE international conference of computer vision and pattern recognition, pp 6936–6944

Liu S, Qi L, Qin H, Shi J, Jia J (2018b) Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759–8768

Liu X, Liang D, Yan S, Chen D, Qiao Y, Yan J (2018c) FOTS: fast oriented text spotting with a unified network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5676–5685

Liu Y, Jin L, Zhang S, Luo C, Zhang S (2019a) Curved scene text detection via transverse and longitudinal sequence connection. Pattern Recognit 90:337–345CrossRef

Liu Y, Jin L, Xie Z, Luo C, Zhang S, Xie L (2019b) Tightness-aware evaluation protocol for scene text detection. In: IEEE Conference on computer vision and pattern recognition, pp 9612–9620

Liu F, Chen C, Gu D, Zheng J (2019c) FTPN: scene text detection with feature pyramid based text proposal network. IEEE Access 7:44219–44228CrossRef

Liu X, Meng G, Pan C (2019d) Scene text detection and recognition with advances in deep learning: a survey. Int J Doc Anal Recognit 22(2):143–162CrossRef

Liu Z, Lin G, Yang S, Liu F, Lin W, Goh WL (2019e) Towards robust curve text detection with conditional spatial expansion. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7269–7278

Liu Y, Zhang S, Jin L, Xie L, Wu Y, Wang Z (2019f) Omnidirectional scene text detection with sequential-free box discretization. In: arXiv:1906.02371

Liu X, Zhang R, Zhou Y, Jiang Q, Song Q, Li N, Zhou K, Wang L, Wang D, Liao M, Yang M (2019g) ICDAR 2019 robust reading challenge on reading chinese text on signboard. In: arXiv:1912.09641

Liu J, Liu X, Sheng J, Liang D, Li X, Liu Q (2019h) Pyramid mask text detector. In: arXiv:1903.11800

Liu H, Guo A, Jiang D, Hu Y, Ren B (2020a) PuzzleNet: scene text detection by segment context graph learning. In: arXiv:2002.11371

Liu Y, Chen H, Shen C, He T, Jin L, Wang L (2020b) ABCNet: real-time scene text spotting with adaptive bezier-curve network. In: arXiv:2002.10200

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE international conference on computer vision and pattern recognition, pp 3431–3440

Long S, Ruan J, Zhang W, He X, Wu W, Yao C (2018a) TextSnake: a flexible representation for detecting text of arbitrary shapes. In: European conference on computer vision, pp 20–36

Long S, He X, Ya C (2018b) Scene text detection and recognition: the deep learning era. In: arXiv:1811.04256

Lu S, Chen T, Tian S, Lim JH, Tan CL (2015) Scene text extraction based on edges and support vector regression. Int J Doc Anal Recognit 18(2):125–135CrossRef

Lucas SM (2005) ICDAR 2005 text locating competition results. In: 8th international conference on document analysis and recognition, pp 80–84

Lucas SM, Panaretos A, Sosa L, Tang A, Wong S, Young R (2003) ICDAR 2003 robust reading competitions. In: 7th international conference on document analysis and recognition, pp 682–687

Lyu P, Yao C, Wu W, Yan S, Bai X (2018a) Multi-oriented scene text detection via corner localization and region segmentation. In: IEEE conference on computer vision and pattern recognition, pp 7553–7563

Lyu P, Liao M, Yao C, Wu W, Bai X (2018b) Mask textspotter: an end-to-end trainable neural network for spotting text with arbitrary shapes. In: Proceedings of the European conference on computer vision, pp 67–83

Ma J, Shao W, Ye H, Wang L, Wang H, Zheng Y, Xue X (2018) Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans Multimed 20(11):3111–3122CrossRef

Ma C, Sun L, Zhong Z, Huo Q (2020) ReLaText: exploiting visual relationships for arbitrary-shaped scene text detection with graph convolutional networks. In: arXiv:2003.06999

Maitra DS, Bhattacharya U, Parui SK (2015) CNN based common approach to handwritten character recognition of multiple scripts. In: 13th international conference on document analysis and recognition, pp 1021–1025

Majhi B, Pujari P (2018) On development and performance evaluation of novel odia handwritten digit recognition methods. Arab J Sci Eng 43(8):3887–3901CrossRef

Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 7:674–693MATHCrossRef

Manjusha K, Kumar MA, Soman KP (2018) Reduced scattering representation for Malayalam character recognition. Arab J Sci Eng 43(8):4315–4326CrossRef

Mishra A, Alahari K, Jawahar CV (2012) Scene text recognition using higher order language priors. In: HAL

Mitchell T (1999) The 20 newsgroups text dataset

Mollah AF, Basu S, Nasipuri M (2012) Text detection from camera captured images using a novel fuzzy-based technique. In: 3rd international conference on emerging applications of information technology, pp 291–294

Mosleh A, Bouguila N, Hamza AB (2012) Image text detection using a bandlet-based edge detector and stroke width transform. In: British machine vision conference, pp 1–12

Nayef N, Yin F, Bizid I, Choi H, Feng Y, Karatzas D, Luo Z, Pal U, Rigaud C, Chazalon J, Khlif W (2017) ICDAR 2017 robust reading challenge on multi-lingual scene text detection and script identification-rrc-mlt. In: 14th IAPR international conference on document analysis and recognition, pp 1454–1459

Nayef N, Patel Y, Busta M, Chowdhury PN, Karatzas D, Khlif W, Matas J, Pal U, Burie JC, Liu CL, Ogier JM (2019) ICDAR 2019 robust reading challenge on multi-lingual scene text detection and recognition–RRC-MLT-2019. In: IAPR international conference of document analysis and recognition

Neumann L, Matas J (2010) A method for text localization and recognition in real-world images. In: Asian conference on computer vision, pp 770–783

Neumann L, Matas J (2012) Real-time scene text localization and recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3538–3545

Neycharan JG, Ahmadyfard A (2018) Edge color transform: a new operator for natural scene text localization. Multimed Tools Appl 77(6):7615–7636CrossRef

Niconico. http://www.nicovideo.jp

Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision, pp 1520–1528

Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59CrossRef

Pan YF, Hou X, Liu CL (2010) A hybrid approach to detect and localize texts in natural scene images. IEEE Trans Image Process 20(3):800–813MathSciNetMATH

Paul S, Saha S, Basu S, Saha PK, Nasipuri M (2019) Text localization in camera captured images using fuzzy distance transform based adaptive stroke filter. Multimed Tools Appl 78(13):18017–18036CrossRef

Qiao L, Tang S, Cheng Z, Xu Y, Niu Y, Pu S, Wu F (2020) Text perceptron: towards end-to-end arbitrary-shaped text spotting. In: arXiv:2002.06820

Qin S, Manduchi R (2017) Cascaded segmentation-detection networks for word-level text spotting. In: 14th international conference on document analysis and recognition, pp 1275–1282

Qin H, Zhang H, Wang H, Yan Y, Zhang M, Zhao W (2019a) An algorithm for scene text detection using multibox and semantic segmentation. Appl Sci 9(6):1054CrossRef

Qin S, Bissacco A, Raptis M, Fujii Y, Xiao Y (2019b) Towards unconstrained end-to-end text spotting. In: Proceedings of the IEEE international conference on computer vision, pp 4704–4714

Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition, pp 779–788

Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

Richardson E, Azar Y, Avioz O, Geron N, Ronen T, Avraham Z, Shapiro S (2019) It’s all about the scale–efficient text detection using adaptive scaling. In: arXiv:1907.12122

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

Saha S, Chakraborty N, Kundu S, Paul S, Mollah AF, Basu S, Sarkar R (2020) Multi-lingual scene text detection and language identification. Pattern Recognit Lett 138:16–22CrossRef

Sain A, Bhunia AK, Roy PP, Pal U (2018) Multi-oriented text detection and verification in video frames and scene images. Neurocomputing 275:1531–1549CrossRef

Sherstinsky A (2018) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. In: arXiv:1808.03314

Shi C, Wang C, Xiao B, Zhang Y, Gao S (2013) Scene text detection using graph model built upon maximally stable extremal regions. Pattern Recognit Lett 34(2):107–116CrossRef

Shi B, Bai X, Belongie S (2017a) Detecting oriented text in natural images by linking segments. In: IEEE international conference on computer vision and pattern recognition, pp 2550–2558

Shi B, Yao C, Liao M, Yang M, Xu P, Cui L, Belongie S, Lu S, Bai X (2017b) ICDAR 2017 competition on reading chinese text in the wild (rctw-17). In: 14th IAPR international conference on document analysis and recognition, pp 1429–1434

Shivakumara P, Phan TQ, Tan CL (2010) A Laplacian approach to multi-oriented text detection in video. IEEE Trans Pattern Anal Mach Intell 33(2):412–419CrossRef

Shivakumara P, Roy S, Jalab HA, Ibrahim RW, Pal U, Lu T, Khare V, Wahab AWBA (2019) Fractional means based method for multi-oriented keyword spotting in video/scene/license plate images. Expert Syst Appl 118:1–19CrossRef

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: arXiv:1409.1556

Song X, Wu Y, Wang W, Lu T (2020) TK-text: multi-shaped scene text detection via instance segmentation. In: Proceedings of the international conference on multimedia modeling, pp 201–213

Sun Y, Zhang C, Huang Z, Liu J, Han J, Ding E (2018) Textnet: irregular text reading from images with an end-to-end trainable network. In: Proceedings of the Asian conference on computer vision, pp 83–99

Sun Y, Liu J, Liu W, Han J, Ding E, Liu J (2019) Chinese street view text: large-scale Chinese text reading with partially supervised learning. In: Proceedings of the IEEE international conference on computer vision, pp 9086–9095

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

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

Tang Y, Wu X (2018) Scene text detection using superpixel-based stroke feature transform and deep learning based region classification. IEEE Trans Multimed 20(9):2276–2288CrossRef

Tang J, Yang Z, Wang Y, Zheng Q, Xu Y, Bai X (2019) SegLink++: detecting dense and arbitrary-shaped scene text by instance-aware component grouping. In: Pattern recognition, vol 96, pp 106954

Tian Z, Huang W, He T, He P, Qiao Y (2016a) Detecting text in natural image with connectionist text proposal network. In: European conference on computer vision, pp 56–72

Tian S, Bhattacharya U, Lu S, Su B, Wang Q, Wei X, Lu Y, Tan CL (2016b) Multilingual scene character recognition with co-occurrence of histogram of oriented gradients. Pattern Recognit 51:125–134CrossRef

Tian Z, Shu M, Lyu P, Li R, Zhou C, Shen X, Jia J (2019) Learning shape-aware embedding for scene text detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4234–4243

Tychsen-Smith L, Petersson L (2017) Denet: scalable real-time object detection with directed sparse sampling. In: IEEE international conference of computer vision, pp 428–436

Van Dongen SM (2000) Graph clustering by flow simulation (Doctoral dissertation)

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

Wang K, Belongie S (2010) Word spotting in the wild. In: European conference on computer vision, pp 591–604

Wang K, Babenko B, Belongie S (2011) End-to-end scene text recognition. In: IEEE international conference on computer vision, pp 1457–1464

Wang T, Wu DJ, Coates A, Ng AY (2012) End-to-end text recognition with convolutional neural networks. In: 21st international conference on pattern recognition, pp 3304–3308

Wang X, Chen K, Huang Z, Yao C, Liu W (2017) Point linking network for object detection. In: arXiv:1706.03646

Wang K, Li G, Liu X, Yan J, Li S, Huang H (2018) Natural scene text detection based on MSER. In: 3rd international conference on communications, information management and network security

Wang X, Feng X, Xia Z (2019a) Scene video text tracking based on hybrid deep text detection and layout constraint. Neurocomputing 363:223–235CrossRef

Wang W, Xie E, Song X, Zang Y, Wang W, Lu T, Yu G, Shen C (2019b) Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: Proceedings of the IEEE international conference on computer vision, pp 8440–8449

Wang P, Zhang C, Qi F, Huang Z, En M, Han J, Liu J, Ding E, Shi G (2019c) A single-shot arbitrarily-shaped text detector based on context attended multi-task learning. In: Proceedings of the 27th ACM international conference on multimedia, pp 1277–1285

Wang X, Jiang Y, Luo Z, Liu CL, Choi H, Kim S (2019d) Arbitrary shape scene text detection with adaptive text region representation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6449–6458

Wang Y, Xie H, Fu Z, Zhang Y (2019e) DSRN: a deep scale relationship network for scene text detection. In: Proceedings of the 28th international joint conference on artificial intelligence. AAAI Press, pp 947–953

Wang H, Lu P, Zhang H, Yang M, Bai X, Xu Y, He M, Wang Y, Liu W (2019f) All you need is boundary: toward arbitrary-shaped text spotting. In: arXiv:1911.09550

Wang S, Liu Y, He Z, Wang Y, Tang Z (2020a) A quadrilateral scene text detector with two-stage network architecture. Pattern Recognit 102:107230CrossRef

Wang Y, Xie H, Zha Z, Xing M, Fu Z, Zhang Y (2020b) ContourNet: taking a further step toward accurate arbitrary-shaped scene text detection. In: arXiv:2004.04940

Welcome to Lasagne. https://lasagne.readthedocs.io/en/latest/

Which GPU(s) to get for deep learning: my experience and advice for using GPUs in deep learning, https://timdettmers.com/2019/04/03/which-gpu-for-deep-learning/. Accessed on 3 June 2020

Wolf C, Jolion JM (2006) Object count/area graphs for the evaluation of object detection and segmentation algorithms. Int J Doc Anal Recognit 8(4):280–296CrossRef

Wu Y, Natarajan P (2017) Self-organized text detection with minimal post-processing via border learning. In: IEEE international conference of computer vision, pp 5000–5009

Xie E, Zang Y, Shao S, Yu G, Yao C, Li G (2019) Scene text detection with supervised pyramid context network. In: Proceedings of the AAAI conference on artificial intelligence, pp 9038–9045

Xu Y, Wang Y, Zhou W, Wang Y, Yang Z, Bai X (2019a) TextField: learning a deep direction field for irregular scene text detection. IEEE Trans Image Process 28(11):5566–5579MathSciNetMATHCrossRef

Xu Y, Duan J, Kuang Z, Yue X, Sun H, Guan Y, Zhang W (2019b) Geometry normalization networks for accurate scene text detection. In: arXiv:1909.00794

Xue C, Lu S, Zhang W (2019) MSR: multi-scale shape regression for scene text detection. In: arXiv:1901.02596

Yang Q, Cheng M, Zhou W, Chen Y, Qiu M, Lin W, Chu W (2018) Inceptext: a new inception-text module with deformable psroi pooling for multi-oriented scene text detection. In: arXiv:1805.01167

Yang P, Zhang F, Yang G (2019) A fast scene text detector using knowledge distillation. IEEE Access 7:22588–22598CrossRef

Yang P, Yang G, Gong X, Wu P, Han X, Wu J, Chen C (2020) Instance segmentation network with self-distillation for scene text detection. IEEE Access 8:45825–45836CrossRef

Yao C, Bai X, Liu W, Ma Y, Tu Z (2012) Detecting texts of arbitrary orientations in natural images. In: IEEE conference on computer vision and pattern recognition, pp 1083–1090

Yao C, Bai X, Sang N, Zhou X, Zhou S, Cao Z (2016) Scene text detection via holistic, multi-channel prediction. In: arXiv:1606.09002

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

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

Zamberletti A, Noce L, Gallo I (2014) Text localization based on fast feature pyramids and multi-resolution maximally stable extremal regions. In: Asian conference on computer vision, pp 91–105

Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. In: 2011 International conference on computer vision, pp 2018–2025

Zhan F, Lu S, Xue C (2018) Verisimilar image synthesis for accurate detection and recognition of texts in scenes. In: Proceedings of the European conference on computer vision, pp 249–266

Zhang Z, Zhang C, Shen W, Yao C, Liu W, Bai X (2016) Multi-oriented text detection with fully convolutional networks. In: IEEE international conference on computer vision and pattern recognition, pp 4159–4167

Zhang S, Wen L, Bian X, Lei Z, Li SZ (2018) Single-shot refinement neural network for object detection. In: IEEE conference on computer vision and pattern recognition, pp 4203–4212

Zhang C, Liang B, Huang Z, En M, Han J, Ding E, Ding X (2019) Look more than once: an accurate detector for text of arbitrary shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 10552–10561

Zhong Z, Jin L, Zhang S, Feng Z (2016) Deeptext: a unified framework for text proposal generation and text detection in natural images. arXiv:1605.07314

Zhong Z, Sun L, Huo Q (2019a) An anchor-free region proposal network for Faster R-CNN based text detection approaches. Int J Doc Anal Recognit 22(3):315–327CrossRef

Zhong Z, Sun L, Huo Q (2019b) Improved localization accuracy by LocNet for faster R-CNN based text detection in natural scene images. In: Pattern recognition, p 106986

Zhou X, Yao C, Wen H, Wang Y, Zhou S, He W, Liang J (2017) EAST: an efficient and accurate scene text detector. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5551–5560

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

Zhu Y, Ma C, Du J (2019) Rotated cascade R-CNN: a shape robust detector with coordinate regression. In: Pattern recognition, vol 96

Titel: Deep learning approaches to scene text detection: a comprehensive review
verfasst von: Tauseef Khan
Ram Sarkar
Ayatullah Faruk Mollah
Publikationsdatum: 01.01.2021
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 5/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-020-09930-6

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