Slab Region Localization for Text Extraction using SIFT Features

문자열 검출을 위한 슬라브 영역 추정

  • 최종현 (포항공교 전자전기공학과) ;
  • 최성후 (포항공교 전자전기공학과) ;
  • 윤종필 (포항공교 전자전기공학과) ;
  • 구근휘 (포항공교 전자전기공학과) ;
  • 김상우 (포항공교 전자전기공학과)
  • Published : 2009.05.01


In steel making production line, steel slabs are given a unique identification number. This identification number, Slab management number(SMN), gives information about the use of the slab. Identification of SMN has been done by humans for several years, but this is expensive and not accurate and it has been a heavy burden on the workers. Consequently, to improve efficiency, automatic recognition system is desirable. Generally, a recognition system consists of text localization, text extraction, character segmentation, and character recognition. For exact SMN identification, all the stage of the recognition system must be successful. In particular, the text localization is great important stage and difficult to process. However, because of many text-like patterns in a complex background and high fuzziness between the slab and background, directly extracting text region is difficult to process. If the slab region including SMN can be detected precisely, text localization algorithm will be able to be developed on the more simple method and the processing time of the overall recognition system will be reduced. This paper describes about the slab region localization using SIFT(Scale Invariant Feature Transform) features in the image. First, SIFT algorithm is applied the captured background and slab image, then features of two images are matched by Nearest Neighbor(NN) algorithm. However, correct matching rate can be low when two images are matched. Thus, to remove incorrect match between the features of two images, geometric locations of the matched two feature points are used. Finally, search rectangle method is performed in correct matching features, and then the top boundary and side boundaries of the slab region are determined. For this processes, we can reduce search region for extraction of SMN from the slab image. Most cases, to extract text region, search region is heuristically fixed [1][2]. However, the proposed algorithm is more analytic than other algorithms, because the search region is not fixed and the slab region is searched in the whole image. Experimental results show that the proposed algorithm has a good performance.


Slab Region Extraction;Search Rectangle Method;SIFT Features;Steel Image;Management Number


  1. S.H. Choi, J.P.Yun, K.H.Koo, J.H.Choi, and S.W.Kim, 'Text Region Extraction Algorithm on Steel Making Process,' 8th WSEAS International Conference on ROBOTICS, CONTROL and MANUFACTURING TECHNOLOGY, pp.24-28, 2008
  2. Lowe, D.G. 'Distinctive Image Features from Scale Invariant Keypoints.' International Journal of Computer Vision, pp.91-110, 2004
  3. Lowe, D.G. 'Object recognition from local scale-invariant features.' Proceedings of International Conference on Computer Vision, pp.1150-1157, 1999
  4. K. Jung, K.I. Kim, and A.K. Jain, 'Text information extraction in images and video: A survey', Pattern Recognition, Vol.37, no.5, pp.977-997, May 2004
  5. P.V.C. Hough. Method and means of recognizing complex patterns, December 1692, U.S. Patent 30695418
  6. Q. Ye, Q. Huang, W. Gao, and D. Zhao, 'Fast and robust text detection in images and video frames', Image and Vision Computing, Vol.23, No.6, 2005, pp.565-576
  7. C.J. Harris and M. Stephens, A combined comer and edge detector, In Proceeding of 4th Alvey Vision Conference, pp.147-151, Manchester, 1988
  8. Yingzi Du, Chein-I Chang, 'Automated system for text detection in individual video images.', Journal of Electronic Imaging, 12(3), 2003, pp.410-422
  9. S.H. Choi, J.P.Yun, K.H.Koo, J.H.Choi, and S.W.Kim, 'An Improved edge-based Text region Segmentation algorithm applied to Slab image data from Steel Plant.', Proceedings of the 10th IASTED International Conference COMPUTER GRAPHICS AND IMAGING, pp.70-75, 2008
  10. Beis, Jeff, and David G.Lowe, 'Shape indexing using approximate nearest neighbour search in high-dimensional spaces,' Conference on Computer Vision and Pattern Recognition, PuertoRico(1997), pp.1000-1006
  11. J. Gao, J, Yang, 'An Adaptive Algorithm for Text Detection from Natural Scenes.', Proceedings of the 2001 IEEE Conference on Computer Vision and Pattern Recognition, 2001
  12. Xilin Chen, Jie Yang, Jing Zhang, Alex Waibel, 'Automatic Detection and Recognition of Signs From Natural Scenes.', IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol. 13, NO.1, 2004, pp.87-99
  13. M.A.Fischler and R.C.Bolles. Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun. Assoc. Comp. Mach. 24:381-395, 1981