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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2012)
Lluís, G., Karatzas, D.: Textproposals: a text-specific selective search algorithm for word spotting in the wild. Pattern Recogn. 70, 60–74 (2017)
Bosamiya, J.H. et al.: Script independent scene text segmentation using fast stroke width transform and GrabCut. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE (2015)
Sounak, D., et al.: Script independent approach for multi-oriented text detection in scene image. Neurocomputing 242, 96–112 (2017)
Yao, Li, et al.: Characterness: an indicator of text in the wild. IEEE Trans. Image Process. 23(4), 1666–1677 (2014)
Gonzalez, A. et al.: Text location in complex images. In: 2012 21st International Conference on Pattern Recognition (ICPR). IEEE (2012)
Kumar, D., Prasad, M.N., Ramakrishnan, A.G.: Multi-script robust reading competition in ICDAR 2013. In: Proceedings of the 4th International Workshop on Multilingual OCR. ACM (2013)
Gomez, L., Karatzas, D.: Multi-script text extraction from natural scenes. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR). IEEE (2013)
Yin, X.-C., et al.: Multi-orientation scene text detection with adaptive clustering. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1930–1937 (2015)
Aneeshan, S., et al.: Multi-oriented text detection and verification in video frames and scene images. Neurocomputing 275, 1531–1549 (2018)
Xu, H., Xue, L., Su, F.: Scene text detection based on robust stroke width transform and deep belief network. In: Asian Conference on Computer Vision. Springer, Cham (2014)
Huang, W., Qiao, Y., Tang, X.: Robust scene text detection with convolution neural network induced mser trees. In: European Conference on Computer Vision. Springer, Cham (2014)
He, T., et al.: Text-attentional convolutional neural network for scene text detection. IEEE Trans. Image Process. 25(6), 2529–2541 (2016)
Honggang, Z., et al.: Text extraction from natural scene image: a survey. Neurocomputing 122, 310–323 (2013)
Zhu, Y., Yao, C., Bai, X.: Scene text detection and recognition: recent advances and future trends. Front. Comput. Sci. 10(1), 19–36 (2016)
Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1480–1500 (2015)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-1280-9_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1279-3
Online ISBN: 978-981-13-1280-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)