Abstract
In recent decades, Scene Text Extraction and Recognition (STER) has remarkably attracted many researchers from the area of image processing and computer vision. Most of the methods discussed in this paper are subjected to evaluation and validation based on different feature properties such as size, orientation, color information, edges, texture property, connected components, and geometry of scene text. Combining different features like CNN and region-based methods together have gained importance and increase in performance of STER. Critical analysis of several state-of-the-art techniques along with benchmark datasets has been carried out with standard testing procedures and evaluation metrics in order to rationalize the decision of selecting an efficient algorithm for better and fast scene text extraction and recognition. Findings of this paper are discussed exclusively by specifying open area for future research in the field of STER system.
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Roopa, M.J., Mahantesh, K. (2021). An Evaluation and Validation of Contemporary Approaches in Scene Text Extraction and Recognition. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_5
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