Abstract
The serial number recognition has played a crucial role in financial market. And the character segmentation is the most important part for serial number recognition. In this paper, in order to deal with the noises and complex textures of the banknotes, we propose a novel method which is composed of a hybrid binarization algorithm (HybridB) and an adaptive character extraction algorithm (ACE). The HybridB algorithm utilizes both the global information of the whole image as well as the local information of the pixels which can eliminate the interferences of the noises and background. The ACE algorithm adjusts the boundaries of each character by the local contrast average value information and the distance between its gravity and center, and a decreasing step strategy is also employed to optimize the boundaries to get more accurate segmentation results. To validate the performance of the proposed method, we take experiments on the datasets of RMB, GBP and USD. The binarization results and character extraction results show that our proposed method outperforms the other state-of-the-art algorithms in most cases.
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Acknowledgements
This work is supported by National Nature Science Foundation of China (Grant Nos. 61773296); the 111 Programme of Introducing Talents of Discipline to Universities (Grant No. B07037); and Research Fund for Academic Team of Young Scholars at Wuhan University (Project No. Whu2016013).
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Zhou, J., Wang, F., Xu, J. et al. A novel character segmentation method for serial number on banknotes with complex background. J Ambient Intell Human Comput 10, 2955–2969 (2019). https://doi.org/10.1007/s12652-018-0707-5
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DOI: https://doi.org/10.1007/s12652-018-0707-5