Abstract
Detecting texts from natural scene images is currently becoming a popular trend in the field of information retrieval. Researchers find it interesting due to the challenges faced while processing an image. In this paper, a relatively simple but effective approach is proposed where bright texts on a dark background and dark texts on a bright background are detected in natural scene images. This approach is based on the fact that there is usually stark contrast between the background and foreground. Hence, K-means clustering algorithm is applied on the gray levels of the image where bright and dark gray level clusters are generated. Each of these clusters are then analyzed to extract the text components. This method proves to be robust compared to the existing methods, giving reasonably satisfactory results when evaluated on Multi-lingual standard datasets like KAIST and MLe2e, and an in-house dataset of images having Multi-lingual texts written in English, Bangla and Hindi.
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Acknowledgements
This work is partially supported by the CMATER research laboratory of the Computer Science and Engineering Department, Jadavpur University, India, PURSE-II and UPE-II, project. SB is partially funded by DBT grant (BT/PR16356/BID/7/596/2016). RS, SB and AFM are partially funded by DST grant (EMR/2016/007213).
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Chakraborty, N., Mollah, A.F., Basu, S., Sarkar, R. (2021). A “Bright-on-Dark, Dark-on-Bright” Approach to Multi-lingual Scene Text Detection. In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol 1255. Springer, Singapore. https://doi.org/10.1007/978-981-15-7834-2_18
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DOI: https://doi.org/10.1007/978-981-15-7834-2_18
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