Classification-Based Character Segmentation of Image

Abstract:

In traditional way, the segmentation of image is conducted by simple technology of image processing, which cannot be operated automatically. In this paper, we present a kind of classification method to find the boundary area to segment character image. Referring to sample points and sample areas, the essential segmentation information is extracted. By merging different formats of image transformation, including rotation, erosion and dilation, more features are used to train and test the segmentation model. Parameter tuning is also proposed to optimize the model for promotion. By the means of cross validation, the basic training model and parameter tuning are integrated in iteration way. The comparison results show that the best precision and recall can up to 97.84% in precision and 94.09% in recall.

• Periodical: Applied Mechanics and Materials (Volumes 519-520)
• Pages: 572-576
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.519-520.572
• Online since: February 2014
• Authors: Yuan Chun Hu, Jian Sun*, Wei Liu
• Keywords: Boundary Detection, Classification, Cross Validation, OCR, Segmentation

References

[1] S. Zhang, M. Zhang and X. Ye, Car Plate Character Extraction under Complicated Environment, in Proceedings of 2004 IEEE International Conference on Systems, Man and Cybernetics, 2004, pp.4722-4726.

DOI: 10.1109/icsmc.2004.1401277

[2] S. Nomura, K. Yamanaka, O. Katai, H. Kawakami, and T. Shiose, A Novel Adaptive Morphological Approach for Degraded Character Image Segmentation, Pattern Recognition, vol. 38, no. 11, 2005, p.1961-(1975).

DOI: 10.1016/j.patcog.2005.01.026

[3] Y. Zhang and C. Zhang, A New Algorithm for Character Segmentation of License Plate, in Proceedings of International Conference on Intelligence Transportation System, 2003, pp.106-109.

[4] X. Shi, W. Zhao and Y. Shen, Automatic License Plate Recognition System Based on Color Image Processing, Computational Science and Its Applications-ICCSA 2005, vol. 3483, 2005, pp.1159-1168.

DOI: 10.1007/11424925_121

[5] C. Gu and W. Ge, Character Recognition Based on Template Matching Method, Communications Technology, vol. 3, 2009, pp.220-222.

[6] S. Nomura, K. Yamanaka, O. Katai, H. Kawakamia and T. Shiose, A Novel Adaptive Morphological Approach for Degraded Character Image Segmentation, Pattern Recognition, vol. 38, no. 11, 2005, p.1961–(1975).

DOI: 10.1016/j.patcog.2005.01.026

[7] A. Capar and M. Gokmen, Concurrent Segmentation and Recognition with Shape-driven Fast Marching Methods, in Proceedings of 18th ICPR, Hong Kong, 2006, pp.155-158.

DOI: 10.1109/icpr.2006.400

[8] V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, and B. P. Feuston, Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling, Journal of Chemical Information and Computer Sciences, 2003, vol. 43, p.1947-(1958).

DOI: 10.1021/ci034160g

[9] Quinlan JR, Induction of Decision trees, Machine Learning, 1986, vol. 1, no. 1, pp: 81-106.

[10] M. Andrew and N. kamal, A Comparison of Event Models for Naïve Bayes text classification, in Proceedings of AAAi-98 workshop on learning for text categorization, (1998).

[11] Y. Freund, R. Schapire and N. Abe, A Short Introduction to Boosting, Journal of Japanese Society for Artificial Intelligence, 1999, vol. 14, p.1612.

[12] C. Cortes and V. Vapnik, Support Vector Networks, Machines Learning, vol. 20, (1995).