Extracting and Segmenting Container Name from Container Images

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International Journal of Computer Applications (0975 – 8887) Volume 74– No.19, July 2013 Fig 8:Some example of container name extraction and segmentation 21
International Journal of Computer Applications (0975 – 8887) Volume 74– No.19, July 2013 4. COMPARISON WITH OTHER TEXT EXTRACTION TECHNIQUES To evaluate the performance there is a comparison with two existing algorithms [9] and [10].The first method has used a complicated method for finding inner,outer and inner-outer corners. The second procedure has extracted text by identify edge at different orientation i.e 0,45,90,135 degrees and grouping these strokes at different heights.So it is difficult to identify the edges at different orientation.To solve the problems Conneted Component Variance(CCV) has used. For finding a meaningfull word the isolated characters are grouped by dilation operation.The algorithm identifies each connected component and measures there properties.If the properties are not match with the properties in section 2.4 then it is symbol not character. 5. CONCLUSION In this paper,we present a algorithm which is suitable to extract the container name of different colors,sizes and alignment modes.The experiments with container images captured from the real port environment demonstrate the effectiveness of the proposed technique. Future work will focus on extracting container name from multiple images captured from the same container with one or multiple cameras during its moving.The container images are captured at the different positions or different time. 6. REFERENCES [1] K. Jain and B. Yu, “Document representation and its application to page decomposition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 294–308, Mar. 1998. [2] J. Ohya, A. Shio, and S. Akamatsu, “Recognizing characters in scene images,” IEEE Trans. Pattern Anal. Machine Intell., vol. 16, pp. 215–220, Feb. 1994. [3] H. M. Suen and J. F.Wang, “Text string extraction from images of colorpriented documents,” Proc. Inst. Elect. Eng. Vis.,Image, Signal Process., vol. 143, no. 4, pp.210–216,1996. [4] Nobuyuki Otsu, A threshold selection method from graylevel histograms. IEEE Trans.Sys.,Man., Cyber 9(1):62- 66. [5] Y. K. Ham, M. S. Kang, H. K. Chung, and R. H. Park, “Recognition of raised characters for automatic classification of rubber tires,” Opt. Eng.,vol. 34, pp. 102– 108, Jan. 1995. [6] R. Lienhart, “Indexing and retrieval of digital video sequences based on automatic text recognition,” in Proc. ACM Int. Conf., Boston, MA, Nov.1996. [7] L. Wang and T. Pavlidis, “Direct gray-scale extraction of features for character recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol.15, pp. 1053–1067, Oct. 1993. [8] Y. Zhong, K. Karu, and A. K. Jain, “Locating text in complex color images,” Pattern Recognit., vol. 28, no. 10, pp. 1523–1535,1995. [9] Jagath Samarabandu, Member, IEEE, and Xiaoqing Liu 2007 “An Edge-based Text Region Extraction Algorithm for Indoor Mobile Robot Navigation” ,International Journal of Signal Processing 3(4 )2007. [10] Yassin M. Y. Hasan and Lina J. Karam 2000 “Morphological Text Extraction from Images” IEEE Transaction on Image Processing vol. 9,no.11,Nov.2000 IJCA TM : www.ijcaonline.org 22
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