Abstract
Microblogging sites are very popular sources of real-time information, which includes both textual information, images, and videos. Since individual posts on such sites are very small, they can convey only a small amount of information. Hence, in situations like an ongoing emergency or disaster, users wishing to share a large amount of information often resort to including the text in an image and then sharing the image. Utilizing such textual information within images requires a text-detection mechanism that not only needs to be accurate, but also very fast in order to process the hundreds of images posted on social media in real-time. In this work, we propose such a text-detection algorithm from images. Experiments over images posted on Twitter during a recent disaster event show that the proposed method achieves competitive accuracy with a state-of-the-art method, while being much faster.
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Acknowledgements
Corresponding author is thankful to Prof. Asit Kumar Das, Prof. Apurba Sarkar and Prof. Surajeet Ghosh from the Department of CST, IIEST Shibpur, Howrah, India for their recommendation letter Dated: 30/04/2019 for using publicly available Twitter data in this paper. The data collected does not have any conflict of interest and has been used ethically.
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Layek, A.K., Mandal, S., Ghosh, S. (2020). A Fast Approach for Text Region Detection from Images on Online Social Media. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_31
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DOI: https://doi.org/10.1007/978-981-13-9042-5_31
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