TextProposals: A text-specific selective search algorithm for word spotting in the wild

Pattern Recognition

Volumn 70, Issue , 2017, Pages 60-74

  Gómez, Lluís ^a   Karatzas, Dimosthenis ^a  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

Author keywords

Grouping; Object proposals; Perceptual organization; Scene text

Indexed keywords

ALUMINUM; BENCHMARKING; DATA MINING; SILICATE MINERALS;

COMPETITIVE PERFORMANCE; END-TO-END SYSTEMS; GROUPING; OBJECT PROPOSALS; PERCEPTUAL ORGANIZATION; SCENE TEXT; SEARCH ALGORITHMS; WORD RECOGNITION;

CHARACTER RECOGNITION;

EID: 85019970310     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2017.04.027     Document Type: Article

References (54)

1. Goel, V., Mishra, A., Alahari, K., Jawahar, C., Whole is greater than sum of parts: Recognizing scene text words. Proceedings of the Twelfth International Conference on Document Analysis and Recognition (ICDAR), 2013, IEEE, 398–402.
2. Almazán, J., Gordo, A., Fornés, A., Valveny, E., Word spotting and recognition with embedded attributes. IEEE Trans. Pattern Anal. Mach. Intell. 36:12 (2014), 2552–2566.
3. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A., Reading text in the wild with convolutional neural networks. Int. J. Comput. Vis. 116:1 (2016), 1–20.
4. Karatzas, D., ICDAR 2015 competition on robust reading. Proceedings of the Thirteenth International Conference on Document Analysis and Recognition (ICDAR), 2015, IEEE, 1156–1160.
5. 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. Proceedings of the 2011 International Conference on Document Analysis and Recognition (ICDAR), 2011, IEEE, 440–445.
6. Wang, T., Wu, D.J., Coates, A., Ng, A.Y., End-to-end text recognition with convolutional neural networks. Proceedings of the Twenty-first International Conference on Pattern Recognition (ICPR), 2012, IEEE, 3304–3308.
7. Jaderberg, M., Vedaldi, A., Zisserman, A., Deep features for text spotting. Proceedings of the 2014 European Conference on Computer Vision (ECCV), 2014, Springer, 512–528.
8. Neumann, L., Matas, J., Real-time scene text localization and recognition. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, IEEE, 3538–3545.
9. Neumann, L., Matas, J., Scene text localization and recognition with oriented stroke detection. Proceedings of the 2013 IEEE International Conference on Computer Vision, 2013, 97–104.
10. Girshick, R., Donahue, J., Darrell, T., Malik, J., Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, 580–587.
11. Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W., Selective search for object recognition. Int. J. Comput. Vis. 104:2 (2013), 154–171.
12. Dollár, P., Appel, R., Belongie, S., Perona, P., Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36:8 (2014), 1532–1545.
13. Zitnick, C.L., Dollár, P., Edge boxes: locating object proposals from edges. Proceedings of the 2014 European Conference on Computer Vision (ECCV), 2014, Springer, 391–405.
14. Kim, K., Byun, H., Song, Y., Choi, Y.-W., Chi, S., Kim, K.K., Chung, Y., Scene text extraction in natural scene images using hierarchical feature combining and verification. Proceedings of the Seventeenth International Conference on Pattern Recognition (ICPR), 2, 2004, IEEE, 679–682.
15. Lee, S., Seok, J., Min, K., Kim, J., Scene text extraction using image intensity and color information. Proceedings of the 2009 Chinese Conference on Pattern Recognition (CCPR), 2009, IEEE, 1–5.
16. Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z., Detecting texts of arbitrary orientations in natural images. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, IEEE, 1083–1090.
17. Koo, H.I., Kim, D.H., Scene text detection via connected component clustering and nontext filtering. IEEE Trans. Image Process. 22:6 (2013), 2296–2305.
18. Yin, X.-C., Yin, X., Huang, K., Hao, H.-W., Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36:5 (2014), 970–983.
19. Gomez, L., Karatzas, D., A fast hierarchical method for multi-script and arbitrary oriented scene text extraction. Int. J. Doc. Anal. Recognit. (IJDAR) 19:4 (2016), 335–349.
20. Ye, Q., Doermann, D., Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37:7 (2015), 1480–1500.
21. Zhu, Y., Yao, C., Bai, X., Scene text detection and recognition: recent advances and future trends. Front. Comput. Sci. 10:1 (2016), 19–36.
22. Jung, K., Kim, K.I., Jain, A.K., Text information extraction in images and video: a survey. Pattern Recognit. 37:5 (2004), 977–997.
23. Liang, J., Doermann, D., Li, H., Camera-based analysis of text and documents: a survey. Int. J. Doc. Anal. Recognit. (IJDAR) 7:2–3 (2005), 84–104.
24. Wang, K., Babenko, B., Belongie, S., End-to-end scene text recognition. Proceedings of the 2011 IEEE International Conference on Computer Vision, 2011, IEEE, 1457–1464.
25. Mishra, A., Alahari, K., Jawahar, C., Top-down and bottom-up cues for scene text recognition. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, IEEE, 2687–2694.
26. Minetto, R., Thome, N., Cord, M., Leite, N.J., Stolfi, J., T-HOG: an effective gradient-based descriptor for single line text regions. Pattern Recognit. 46:3 (2013), 1078–1090.
27. Yao, C., Bai, X., Liu, W., A unified framework for multioriented text detection and recognition. IEEE Trans. Image Process. 23:11 (2014), 4737–4749.
28. Epshtein, B., Ofek, E., Wexler, Y., Detecting text in natural scenes with stroke width transform. Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, IEEE, 2963–2970.
29. Mosleh, A., Bouguila, N., Hamza, A.B., Image text detection using a bandlet-based edge detector and stroke width transform. Proceedings of the 2012 British Machine Vision Conference (BMVC), 2012, 1–12.
30. Xu, H., Xue, L., Su, F., Scene text detection based on robust stroke width transform and deep belief network. Proceedings of the 2014 Asian Conference on Computer Vision (ACCV), 2014, Springer, 195–209.
31. Matas, J., Chum, O., Urban, M., Pajdla, T., Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22:10 (2004), 761–767.
32. Neumann, L., Matas, J., A method for text localization and recognition in real-world images. Proceedings of the 2010 Asian Conference on Computer Vision (ACCV), 2010, Springer, 770–783.
33. Chen, H., Tsai, S.S., Schroth, G., Chen, D.M., Grzeszczuk, R., Girod, B., Robust text detection in natural images with edge-enhanced maximally stable extremal regions. Proceedings of the Eighteenth IEEE International Conference on Image Processing (ICIP), 2011, IEEE, 2609–2612.
34. Novikova, T., Barinova, O., Kohli, P., Lempitsky, V., Large-lexicon attribute-consistent text recognition in natural images. Proceedings of the 2012 European Conference on Computer Vision (ECCV), 2012, Springer, 752–765.
35. Shi, C., Wang, C., Xiao, B., Zhang, Y., Gao, S., Scene text detection using graph model built upon maximally stable extremal regions. Pattern Recognit. Lett. 34:2 (2013), 107–116.
36. Shi, C., Wang, C., Xiao, B., Gao, S., Hu, J., End-to-end scene text recognition using tree-structured models. Pattern Recognit. 47:9 (2014), 2853–2866.
37. Alsharif, O., Pineau, J., End-to-end text recognition with hybrid HMM maxout models. Proceedings of the 2014 International Conference on Learning Representations (ICLR), 2014.
38. Sun, L., Huo, Q., Jia, W., Chen, K., A robust approach for text detection from natural scene images. Pattern Recognit. 48:9 (2015), 2906–2920.
39. Huang, W., Qiao, Y., Tang, X., Robust scene text detection with convolution neural network induced mser trees. Proceedings of the 2014 European Conference on Computer Vision (ECCV), 2014, Springer, 497–511.
40. Hosang, J., Benenson, R., Dollár, P., Schiele, B., What makes for effective detection proposals?. IEEE Trans. Pattern Anal. Mach. Intell. 38:4 (2016), 814–830.
41. Huo, L., Jiao, L., Wang, S., Yang, S., Object-level saliency detection with color attributes. Pattern Recognit. 49 (2016), 162–173.
42. González-Díaz, I., Buso, V., Benois-Pineau, J., Perceptual modeling in the problem of active object recognition in visual scenes. Pattern Recognit. 56 (2016), 129–141.
43. Cheng, M.-M., Zhang, Z., Lin, W.-Y., Torr, P., BING: binarized normed gradients for objectness estimation at 300fps. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, 3286–3293.
44. Manen, S., Guillaumin, M., Gool, L., Prime object proposals with randomized Prim's algorithm. Proceedings of the 2013 IEEE International Conference on Computer Vision, 2013, 2536–2543.
45. Krähenbühl, P., Koltun, V., Geodesic object proposals. Proceedings of the 2014 European Conference on Computer Vision (ECCV), 2014, Springer, 725–739.
46. Borgefors, G., Distance transformations in digital images. Comput. Vis. Graph. Image Process. 34:3 (1986), 344–371.
47. Fitzgibbon, A., Fisher, R.B., A buyer's guide to conic fitting. Proceedings of the 1995 British Machine Vision Conference (BMVC), 1995.
48. Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R., Ashida, K., Nagai, H., Okamoto, M., Yamamoto, H., et al. ICDAR 2003 robust reading competitions: entries, results, and future directions. Int. J. Doc. Anal. Recognit. (IJDAR) 7:2–3 (2005), 105–122.
49. Karatzas, D., ICDAR 2013 robust reading competition. Proceedings of the Twelfth International Conference on Document Analysis and Recognition (ICDAR), 2013, IEEE, 1484–1493.
50. Wang, K., Belongie, S., Word spotting in the wild. Proceedings of the Eleventh European Conference on Computer Vision: Part I, 2010, Springer-Verlag, 591–604.
51. Gomez, L., Karatzas, D., A fine-grained approach to scene text script identification. Proceedings of the Twelfth IAPR International Workshop on Document Analysis Systems (DAS), 2016, IEEE.
52. Cao, F., Delon, J., Desolneux, A., Musé, P., Sur, F., An a Contrario Approach to Hierarchical Clustering Validity Assessment. 2004, INRIA Research Report.
53. Gómez, L., Karatzas, D., Scene text recognition: no country for old men?. Proceedings of the 2014 Workshop on Asian Conference on Computer Vision (ACCV), 2014, Springer, 157–168.
54. Liu, X., Lu, T., Natural scene character recognition using Markov random field. Proceedings of the Thirteenth International Conference on Document Analysis and Recognition (ICDAR), 2015, IEEE, 396–400.