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Topology-Based Character Recognition Method for Coin Date Detection

Authors: Xingyu Pan, Laure Tougne

Abstract:

For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: Coin, detection, character recognition, topology.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127058

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References:


[1] Zambanini, S., Kampel, M.: Improving ancient roman coin classification by fusing exemplar-based classification and legend recognition. In: New Trends in Image Analysis and Processing-ICIAP 2013. Springer (2013) 149-158.
[2] X. Pan, K. Puritat, L. Tougne, A New Coin segmentation and Graph-Based Identification Method for Numismatic Application. ISVC14, Las Vegas, LNCS, Vol.8888 (2014) 185-195.
[3] Fukumi, M., Omatu, S., Takeda, F., Kosaka, T.: Rotation-invariant neural pattern recognition system with application to coin recognition. IEEE Trans. Neural Network 3 (1992) 272-279.
[4] Nolle, M., Penz, H., Rubik, M., Mayer, K., Hollander, I., Granec, R.: Dagobert-a new coin recognition and sorting system. In: Proceedings of the 7th Internation Conference on Digital Image Computing-Techniques and Applications (2003).
[5] Van Der Maaten, L.J., Poon, P.: Coin-o-matic: A fast system for reliable coin classification. In: Proc. of the Muscle CIS Coin Competition Workshop. (2006) 7-18.
[6] Reisert, M., Ronneberger, O., Burkhardt, H.: A fast and reliable coin recognition system. In: Pattern Recognition. Springer (2007) 415-424.
[7] Huber, R., Ramoser, H., Mayer, K., Penz, H., Rubik, M.: Classification of coins using an eigenspace approach. PRL 26 (2005) 61-75.
[8] Kampel, M., Zaharieva, M.: Recognizing ancient coins based on local features. In: Advances in Visual Computing. Springer. (2008) 11-22.
[9] Zambanini, S., Kampel, M.: Automatic coin classification by image matching. In: Proc. of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage. (2011) 65-72.
[10] Zambanini, S., Kampel, M.: Coarse-to-fine correspondence search for classifying ancient coins. ACCV Workshops 2, 25–36 (2012).
[11] Arandjelovic, O.: Automatic attribution of ancient roman imperial coins. In:CVPR10. (2010) 1728-1734.
[12] Anwar, H., Zambanini, S., Kampel, M.: Supporting ancient coin classification by image-based reverse side symbol recognition. CAIP 2, (2013) 17–25.
[13] Anwar, H., Zambanini, S., Kampel, M.: Coarse-grained Ancient Coin Classification using Image-based Reverse Side Motif Recognition, Machine Vision and Applications, 26 (2-3): (2015) 295-304;
[14] Arandjelovic, O.: Reading ancient coins: Automatically identifying denarii using obverse legend seeded retrieval. In: ECCV12. Springer (2012) 317-330.
[15] Kavelar, A., Zambanini, S., Kampel, M.: Word detection applied to images of ancient roman coins. In Virtual Systems and Multimedia (VSMM), 2012 18th International Conference on (pp. 577-580). IEEE.
[16] Pan, Y. F., Hou, X., Liu, C. L. (2011). A hybrid approach to detect and localize texts in natural scene images. IEEE Transactions on Image Processing, 20(3), 800-813.
[17] Epshtein, B., Ofek, E., Wexler, Y. (2010). Detecting text in natural scenes with stroke width transform. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on (pp. 2963-2970). IEEE.
[18] Liu, Y., Ikenaga, T. (2006). A contour-based robust algorithm for text detection in color images. IEICE transactions on information and systems,89(3), 1221-1230.
[19] Zhu, A., Wang, G., Dong, Y. (2015). Detecting natural scenes text via auto image partition, two-stage grouping and two-layer classification. Pattern Recognition Letters, 67, 153-162.
[20] Sun, L., Huo, Q., Jia, W., Chen, K. (2015). A robust approach for text detection from natural scene images. Pattern Recognition, 48(9), 2906-2920.
[21] Trier, Ø. D., Jain, A. K., Taxt, T. (1996). Feature extraction methods for character recognition-a survey. Pattern recognition, 29(4), 641-662.
[22] Thome, N., Vacavant, A., Robinault, L., Miguet, S. A cognitive and video-based approach for multinational license plate recognition. Machine Vision and Applications, 22(2), (2011) 389-407.