Video Text Detection and Recognition for Marketing using Deep Learning Technique
Keywords:
Video Text Detection, Recognition, Transferred Convolutional Neural Network, Fuzzy c-means Clustering.Abstract
Marketing analytics is the practice of managing and studying metrics data in order to determine the return on investment (ROI) of marketing efforts such as calls-to-action (CTAs), blog posts, channel performance, and thought leadership pieces, and to identify opportunities for improvement. By tracking and reporting on business performance data, diagnostic metrics, and leading indicator metrics, marketers are able to provide answers to the analytics questions that are most vital to their stakeholders. In this paper, we propose a novel method that transfers deep convolutional neural networks for detecting and recognizing video text. We partition the candidate text regions into candidate text lines by projection analysis using two alternative methods. We develop a novel fuzzy c-means clustering-based separation algorithm to obtain a clean text layer from complex backgrounds so that the text is correctly recognized by commercial optical character recognition software. The proposed method is robust and demonstrates good performance on video text detection and recognition, which was evaluated on three publicly available test datasets and on a high-resolution test dataset constructed by the authors.
References
Y. A. Aslandogan and C. T. Yu, “Techniques and systems for image and video retrieval,” IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 1, pp. 56–63, Jan. 1999.
H. Bhaskar and L. Mihaylova, “Combined feature-level video indexing using block-based motion estimation,” in Proceedings of the Conference on Information Fusion, Jul. 2010, pp. 1–8.
W. Hu, N. Xie, L. Li, X. Zeng, and S. Maybank, “A survey on visual content-based video indexing and retrieval,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 41, no. 6, pp. 797–819, Nov. 2011.
K. Jung, K. I. Kim, and A. K. Jain, “Text information extraction in images and video: A survey,” Pattern Recognition, vol. 37, no. 5, pp. 977–997, 2004.
M. Khodadadi and A. Behrad, “Text localization, extraction and inpainting in color images,” in Proceedings of the Iranian Conference on Electrical Engineering, May 2012, pp. 1035–1040.
A. Mosleh, N. Bouguila, and A. B. Hamza, “Automatic inpainting scheme for video text detection and removal,” IEEE Transactions on Image Processing, vol. 22, no. 11, pp. 4460–4472, Nov. 2013.
K. Jung and H. J. Kim, “Texture-based approach for text detection in images using support vector machines and continuous adaptive mean shift algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1631–1639, Dec. 2003.
M. Cai, J. Song, and M. R. Lyu, “A new approach for video text detection,” IEEE Transactions on Image Processing, vol. 11, no. 9, pp. 1071–1120, Sep. 2002.
T. Tsun, Y. Wang, and X. Fang, “A video text detection and tracking system,” in Proceedings of the ACM International Symposium on Multimedia, Dec. 2013, pp. 522–529.
X. Huang, “A novel video text extraction approach based on Log-Gabor filters,” in Proceedings of the International Conference on Image and Signal Processing, vol. 1, Oct. 2011, pp. 474–478.
P. Shivakumara, W. Huang, and C. L. Tan, “Efficient video text detection using edge features,” in Proceedings of the International Conference on Pattern Recognition, Dec. 2008, pp. 1–4.
X. Zhao, K. H. Lin, Y. Fu, W. Hu, Z. Liu, and S. He, “Text from corners: A novel approach to detect text and caption in videos,” IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 790–799, Mar. 2011.
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
