Information and Decision Sciences pp 519-529 | Cite as
A Novel Arbitrary-Oriented Multilingual Text Detection in Images/Video
Abstract
Text in images and videos plays a vital role to understand the events. The textual information is a prominent source and semantic information of a particular content of the respective image or video. Text detection is a primary stage for text recognition and text understanding. Still, text detection process is a challenging and interesting research work in the field of computer vision due to illumination, alignments, complex background and variation size, color, fonts of the text. The multilingual text consists of different geometrical structures of languages. In this paper, a simple and yet effective approach is presented to detect the text from arbitrary oriented multilingual images/video. The proposed method is based on Laplacian of Gaussian information and full connected component analysis. The proposed method is evaluated on four datasets such as Hua’s dataset, arbitrarily oriented dataset, Multi-script Robust Reading Competition (MRRC) dataset and MSRA dataset with performance measures precision, recall and f-measure. The results show that the proposed method is promising and encouraging.
Keywords
Multilingual text Arbitrary-Oriented Laplacian of Gaussian Full connected componentReferences
- 1.Khare, V., Shivakumara, P., Raveendran, P.: Multi-oriented moving text detection. International Symposium on Intelligent Signal Processing and Communication Systems, pp. 347–352 (2014)Google Scholar
- 2.Minemura, K., Palaiahnakote, S., Wong, K.: Multi-oriented text detection for intra-frame in H. 264/AVC video. International Symposium on Intelligent Signal Processing and Communication Systems, pp. 330–335 (2014)Google Scholar
- 3.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)MathSciNetCrossRefGoogle Scholar
- 4.Wu, H., Zou, B., Zhao, Y.Q., Guo, J.: Scene text detection using adaptive color reduction, adjacent character model and hybrid verification strategy. Vis. Comput. 33(1), 113–126 (2017)CrossRefGoogle Scholar
- 5.Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: EAST: an efficient and accurate scene text detector (2017). arXiv:1704.03155
- 6.Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., Xue, X.: Arbitrary-oriented scene text detection via rotation proposals (2017). arXiv:1703.01086
- 7.Pavithra, M.S., Aradhya, V.N.M.: A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. Appl. Comput. Inform., 1–15 (2014)Google Scholar
- 8.Jeong, M., Jo, K.H.: Multi language text detection using fast stroke width transform. 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision, pp. 1–4 (2015)Google Scholar
- 9.Ren, X., Zhou, Y., Huang, Z., Sun, J., Yang, X., Chen, K.: A novel text structure feature extractor for chinese scene text detection and recognition. IEEE Access 5, 3193–3204 (2017)CrossRefGoogle Scholar
- 10.Liao, W.H., Wu, Y.C.: An integrated approach for multilingual scene text detection. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 8, 033–041 (2016)Google Scholar
- 11.Hua, X.S., Wenyin, L., Zhang, H.J.: An automatic performance evaluation protocol for video text detection algorithms. IEEE Trans. CSVT, 498–507 (2004)Google Scholar
- 12.Lu, C., Wang, C., Dai, R.: Text detection in images based on unsupervised classification of edge-based features. In: Proceedings of ICDAR, pp. 610–614 (2005)Google Scholar
- 13.Multi-script robust reading competition. http://mile.ee.iisc.ernet.in/mrrc/index.html
- 14.Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: Proceedings of CVPR, pp. 1083–1090 (2012)Google Scholar
- 15.Shivakumara, P., Basavaraju, H.T., Guru, D.S., Tan, C.L.: Detection of curved text in video: quad tree based method. In: 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 594–598 (2013)Google Scholar
- 16.Sharma, N., Shivakumara, P., Pal, U., Blumenstein, M., Tan, C.L.: A new method for arbitrarily-oriented text detection in video. In: Proceedings of DAS, pp. 74–78 (2012)Google Scholar
- 17.Shivakumara, P., Sreedhar, R.P., Phan, T.Q., Lu, S., Tan, C.L.: Multi-oriented video scene text detection through Bayesian classification and boundary growing. IEEE Trans. CSVT, 1227–235 (2012)CrossRefGoogle Scholar
- 18.Shivakumara, P., Phan, T. Q., Tan, C.L.: A laplacian approach to multi-oriented text detection in video. IEEE Trans. PAMI, 412–419 (2011)Google Scholar
- 19.Zhou, J., Xu, L., Xiao, B., Dai, R.: A robust system for text extraction in video. In: Proceedings of ICMV, pp. 119–124 (2007)Google Scholar
- 20.Shivakumara, P., Phan, T.Q., Tan, C.L.: New Fourier-statistical features in RGB space for video text detection. IEEE Trans. CSVT, 1520–1532 (2010)Google Scholar
- 21.Lu, C., Wang, C., Dai, R.: Text detection in images based on unsupervised classification of edge-based features. In: Proceedings of ICDAR, pp. 610–614 (2005)Google Scholar
- 22.Wong, E.K., Chen, M.: A new robust algorithm for video text extraction. Pattern Recognit., 1397–1406 (2003)Google Scholar
- 23.Cai, M., Song J., Lyu, M.R.: A new approach for video text detection. In: Proceedings of ICIP, pp. 117–120 (2002)Google Scholar
- 24.Dey, S., Shivakumara, P., Raghunandan, K.S., Pal, U., Lu, T., Kumar, G.H., Chan, C.S.: Script independent approach for multi-oriented text detection in scene image. Neurocomputing 242, 96–112 (2017)CrossRefGoogle Scholar
- 25.Shivakumara, P., Phan, T.Q., Tan, C.L.: New wavelet and color features for text detection in video. In: Proceedings of ICPR, pp. 3996–3999 (2010)Google Scholar
- 26.Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Computer Vision and Pattern Recognition (CVPR), pp. 2963–2970 (2010)Google Scholar
- 27.Rong, L., Suyu, W., Shi, Z.: A two level algorithm for text detection in natural scene images. In: 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 329–333 (2014)Google Scholar
- 28.Mosleh, A., Bouguila, N., Hamza, A.B.: Automatic inpainting scheme for video text detection and removal. IEEE Trans. Image Process. 32(4), 460–472 (2013)Google Scholar
- 29.Li, Y., Jia, W., Shen, C., Hengel, A.V.D.: Characterness: an indicator of text in the wild. IEEE Trans. Image Process. 23, 1666–1677 (2014)Google Scholar
- 30.Zhao, X., Lin, K.H., Fu, Y., Hu, Y., Liu, Y., Huang, T.S.: Text from corners: a novel approach to detect text and caption in videos. IEEE Trans. Image Proces. 20, 790–799 (2011)Google Scholar
- 31.Yin, X.C., Yin, X., Huang, K., Hao, H.W.: Robust text detection in natural scene images. IEEE Trans. PAMI 36, 970–983 (2014)Google Scholar
- 32.Risnumawan, A., Shivakumara, P., Chan, C.S., Tan, C.L.: A robust arbitrary text detection system for natural scene images. ESWA 41, 8027–8048 (2014)CrossRefGoogle Scholar
- 33.Kang, L., Li, Y., Doermann, D.: Orientation robust text line detection in natural images. In: Proceedings of CVPR, pp. 4034–4041 (2014)Google Scholar
- 34.Yao, C., Bai, X., Liu, W.: A unified framework for multioriented text detection and recognition. IEEE Trans. Image Process. 23, 4737–4749 (2014)MathSciNetCrossRefGoogle Scholar
- 35.Yin, X.C., Pei, W.Y., Zuang, J., Hao, H.W.: Multi-orientation scene text detection with adaptive clustering. IEEE Trans. PAMI 37, 1930–1937 (2015)CrossRefGoogle Scholar