Abstract
Detecting and localizing multi-lingual text regions in natural scene images is a challenging task due to variation in texture properties of the image and geometric properties of multi-lingual text. In this work, we explore the possibility of identifying and localizing text regions based on their degree of homogeneity compared to the non-text regions of the image by binning red, green, blue channels and gray levels into bins represented individually by binary images whose connected components undergo several elimination processes and the possible text regions are distinguished and localized from non-text regions. We evaluated our proposed method on our camera captured image collection having multi-lingual texts in languages namely, English, Bangla, Hindi and Oriya and observed 0.69 as the F-measure value for best case where the image has good number of possible text regions.
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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) and UGC Research Award (F. 30-31/2016(SA-II)). RS, SB and AFM are partially funded by DST grant (EMR/2016/007213). The heading should be treated as a 3rd level heading and should not be assigned a number.
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Dutta, I.N., Chakraborty, N., Mollah, A.F., Basu, S., Sarkar, R. (2019). Multi-lingual Text Localization from Camera Captured Images Based on Foreground Homogenity Analysis. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_15
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DOI: https://doi.org/10.1007/978-981-13-1280-9_15
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