Abstract
Identification of script from multi-script text components of camera-captured images is an emerging research field. Here, challenges are mainly twofold: (1) typical challenges of camera-captured images like blur, uneven illumination, complex background, etc., and (2) challenges related to shape, size, and orientation of the texts written in different scripts. In this work, an effective set consisting of both shape-based and texture-based features is designed for script classification. An in-house scene text data set comprising 300 text boxes written in three scripts, namely Bangla, Devanagri, and Roman is prepared. Performance of this feature set is associated with five popular classifiers and highest accuracy of 90% is achieved with Multi-layer Perceptron (MLP) classifier, which is reasonably satisfactory considering the domain complexity.
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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).
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Jajoo, M., Chakraborty, N., Mollah, A.F., Basu, S., Sarkar, R. (2019). Script Identification from Camera-Captured Multi-script Scene Text Components. 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_16
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DOI: https://doi.org/10.1007/978-981-13-1280-9_16
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