Abstract

Wafer identifications (wafer ID) can be used to identify wafers from each other so that wafer processing can be traced easily. Wafer ID recognition is one of the problems of optical character recognition. The process to recognize wafer IDs is similar to that used in recognizing car license-plate characters. However, due to some unique characteristics, such as the irregular space between two characters and the unsuccessive strokes of wafer ID, it will not get a good result to recognize wafer ID by directly utilizing the approaches used in car license-plate character recognition. Wafer ID scratches are engraved by a laser scribe almost along the following four fixed directions: horizontal, vertical, plus 45°, and minus 45° orientations. The closer to the center line of a wafer ID scratch, the higher the gray level will be. These and other characteristics increase the difficulty to recognize the wafer ID. In this paper a wafer ID recognition scheme based on an asterisk-shape filter and a high–low score comparison method is proposed to cope with the serious influence of uneven luminance and make recognition more efficiently. Our proposed approach consists of some processing stages. Especially in the final recognition stage, a template-matching method combined with stroke analysis is used as a recognizing scheme. This is because wafer IDs are composed of Semiconductor Equipment and Materials International (SEMI) standard Arabic numbers and English alphabets, and thus the template ID images are easy to obtain. Furthermore, compared with the approach that requires prior training, such as a support vector machine, which often needs a large amount of training image samples, no prior training is required for our approach. The testing results show that our proposed scheme can efficiently and correctly segment out and recognize the wafer ID with high performance.

© 2009 Optical Society of America

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  1. W. N. Lie, H. C. Hsieh, C. L. Wu, and H. Y. Chen, “A template-matching-based wafer ID recognition system,” in Proceedings of the 1999 Workshop on Computer Vision, Graphics and Image Processing (CVGIP'99, 1999), pp. 722-727.
  2. C. N. Chen, “Study on wafer identification recognition,” Master's thesis (Tatung University, 2005).
  3. D. U. Cho and Y. H. Cho, “Implementation of preprocessing independent of environment and recognition of car number plate using histogram and template matching,” J. Korean Inst. Commun. Sci. 23, 94-100 (1998).
  4. M. Shridhar, J. W. V. Miller, G. Houle, and L. Bijnagte, “Recognition of license plate images: issues and perspectives,” in Proceedings of the Fifth International Conference on Document Analysis and Recognition (IEEE, 1999), pp. 17-20.
  5. K. M. Kim, B. J. Lee, K. Lyou, and G. T. Park, “The automatic recognition of the plate of vehicle using the correlation coefficient and Hough transform,” J. Contr. Autom. Syst. Eng. 3, 511-519 (1997).
  6. D. H. Ballard and C. M. Brown, Computer Vision (Prentice Hall Professional Technical Reference, 1982).
  7. Y. Liu and S. N. Srihari, “Document image binarization based on texture features,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 540-544 (1997).
    [CrossRef]
  8. L. Fan and C. L. Tan, “Binarizing document image using coplanar prefilter,” in Sixth International Conference on Document Analysis and Recognition (IEEE, 2001), pp. 34-38.
  9. N. Babaguchi, K. Yamada, K. Kise, and Y. Tezuka, “Connectionist model binarization,” in 10th International Conference on Pattern Recognition (IEEE, 1990), pp. 51-56.
    [CrossRef]
  10. H. A. Hegt, R. J. de la Haye, and N. A. Khan, “A high performance license plate recognition system,” in IEEE International Conference on Systems, Man, and Cybernetics (IEEE, 1998), pp. 4357-4362.
  11. L. Salgado, J. M. Menendez, E. Rendon, and N. Garcia, “Automatic car plate detection and recognition through intelligent vision engineering,” in 1999 IEEE International Carnahan Conference on Security Technology (IEEE, 1999), pp. 71-76.
  12. M. H. ter Brugge, J. H. Stevens, J. A. G. Nijhuis, and L. Spaanenburg, “License plate recognition using DTCNNs,” in 1998 Fifth IEEE International Workshop on Cellular Neural Networks and Their Applications: Proceedings (IEEE, 1998), pp. 212-217.
    [CrossRef]
  13. G. Da-Shan and Z. Jie, “Car license plates detection from complex scene,” in 2000 5th International Conference on Signal Processing: Proceedings (IEEE, 2000), pp. 1409-1414.
    [CrossRef]
  14. J. C. H. Poon, M. Ghadiali, G. M. T. Mao, and L. M. Sheung, “A robust vision system for vehicle licence plate recognition using grey-scale morphology,” in Proceedings of the IEEE International Symposium on Industrial Electronics (IEEE, 1995), pp. 394-399.
    [CrossRef]
  15. J. A. G. Nijhuis, M. H. Ter Brugge, K. A. Helmholt, J. P. W. Pluim, L. Spaanenburg, R. S. Venema, and M. A. Westenberg, “Car license plate recognition with neural networks and fuzzy logic,” in 1995 IEEE International Conference on Neural Networks: Proceedings (IEEE, 1995), pp. 2232-2236.
    [CrossRef]
  16. S. K. Kim, D. W. Kim, and H. J. Kim, “A recognition of vehicle license plate using a genetic algorithmbased segmentation,” in 1996 International Conference on Image Processing (IEEE, 1996), pp. 661-664.
  17. K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Neural Networks for Signal Processing X: Proceedings of the 2000 IEEE Signal Processing Society Workshop (IEEE, 2000), pp. 614-623.
    [CrossRef] [PubMed]
  18. R. Parisi, E. D. Di Claudio, G. Lucarelli, and G. Orlandi, “Car plate recognition by neural networks and image processing,” in ISCAS '98, Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (IEEE, 1998), pp. 195-198.
    [CrossRef]
  19. Y. T. Cui and Q. Huang, “Character extraction of license plates from video,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 502-507.
    [CrossRef]
  20. G. Adorni, F. Bergenti, and S. Cagnoni, “Vehicle license plate recognition by means of cellular automata,” in Proceedings of the IEEE International Conference on Intelligent Vehicles (IEEE, 1998), pp. 689-693.
  21. A. Shio and K. Komori, “A stroke extraction method for handprinted Chinese character recognition,” IECE Trans. IE80-14, 83-90 (1980).
  22. C. Chuang and L. Tseng, “A stroke extraction method for multifont Chinese characters basedon the reduced special interval graph,” IEEE Trans. Syst. Man Cybern. 25, 1171-1178 (1995).
    [CrossRef]
  23. W. T. Yang, “Automatic vehicle identification number recognition system,” Master's thesis (Chung Yuan Christian University, 1995).
  24. G. S. Lehal, “Optical character recognition of Gurmukhi script using multiple classifiers,” in Proceedings of the International Workshop on Multilingual OCR (ACM, 2009), pp. 1-9.
    [CrossRef]
  25. M. Hanmandlu and O. Murthy, “Fuzzy model based recognition of handwritten numerals,” Pattern Recognition 40, 1840-1854 (2007).
    [CrossRef]
  26. S. Mahmoud and A. Mahmoud, “Arabic character recognition using modified Fourier spectrum (MFS) vs. Fourier descriptors,” Cybern. Syst. 40, 189-210 (2009).
    [CrossRef]
  27. W. Wen, X. Huang, L. Yang, Z. Yang, and P. Zhang, “Vehicle license plate location method based-on wavelet transform,” in Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization (IEEE, 2009), pp. 381-384.
    [CrossRef] [PubMed]
  28. F. Faradji, A. H. Rezaie, and M. Ziaratban, “A morphological-based license plate location,” in IEEE International Conference on Image Processing (IEEE, 2007), pp. I-57-I-60.
  29. K. Deb, S. Kang, and K. Jo, “Statistical characteristics in HSI color model and position histogram based vehicle license plate detection,” Intell. Serv. Robotics 2, 173-186(2009).
    [CrossRef]
  30. T. L. Lin, “Intelligent license plate searching and content segmentation in image processing,” Master thesis (National Taiwan University, 2000).
  31. B. H. Friemel, L. N. Bohs, and G. E. Trahey, “Relative performance of two-dimensional speckle-tracking techniques: normalized correlation, non-normalized correlation and sum-absolute-difference,” in 1995 IEEE Ultrasonics Symposium: Proceedings (IEEE, 1995), Vol. 1482, pp. 1481-1484.

2009

S. Mahmoud and A. Mahmoud, “Arabic character recognition using modified Fourier spectrum (MFS) vs. Fourier descriptors,” Cybern. Syst. 40, 189-210 (2009).
[CrossRef]

K. Deb, S. Kang, and K. Jo, “Statistical characteristics in HSI color model and position histogram based vehicle license plate detection,” Intell. Serv. Robotics 2, 173-186(2009).
[CrossRef]

2007

M. Hanmandlu and O. Murthy, “Fuzzy model based recognition of handwritten numerals,” Pattern Recognition 40, 1840-1854 (2007).
[CrossRef]

1998

D. U. Cho and Y. H. Cho, “Implementation of preprocessing independent of environment and recognition of car number plate using histogram and template matching,” J. Korean Inst. Commun. Sci. 23, 94-100 (1998).

1997

K. M. Kim, B. J. Lee, K. Lyou, and G. T. Park, “The automatic recognition of the plate of vehicle using the correlation coefficient and Hough transform,” J. Contr. Autom. Syst. Eng. 3, 511-519 (1997).

Y. Liu and S. N. Srihari, “Document image binarization based on texture features,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 540-544 (1997).
[CrossRef]

1995

C. Chuang and L. Tseng, “A stroke extraction method for multifont Chinese characters basedon the reduced special interval graph,” IEEE Trans. Syst. Man Cybern. 25, 1171-1178 (1995).
[CrossRef]

1980

A. Shio and K. Komori, “A stroke extraction method for handprinted Chinese character recognition,” IECE Trans. IE80-14, 83-90 (1980).

Adorni, G.

G. Adorni, F. Bergenti, and S. Cagnoni, “Vehicle license plate recognition by means of cellular automata,” in Proceedings of the IEEE International Conference on Intelligent Vehicles (IEEE, 1998), pp. 689-693.

Babaguchi, N.

N. Babaguchi, K. Yamada, K. Kise, and Y. Tezuka, “Connectionist model binarization,” in 10th International Conference on Pattern Recognition (IEEE, 1990), pp. 51-56.
[CrossRef]

Ballard, D. H.

D. H. Ballard and C. M. Brown, Computer Vision (Prentice Hall Professional Technical Reference, 1982).

Bergenti, F.

G. Adorni, F. Bergenti, and S. Cagnoni, “Vehicle license plate recognition by means of cellular automata,” in Proceedings of the IEEE International Conference on Intelligent Vehicles (IEEE, 1998), pp. 689-693.

Bijnagte, L.

M. Shridhar, J. W. V. Miller, G. Houle, and L. Bijnagte, “Recognition of license plate images: issues and perspectives,” in Proceedings of the Fifth International Conference on Document Analysis and Recognition (IEEE, 1999), pp. 17-20.

Bohs, L. N.

B. H. Friemel, L. N. Bohs, and G. E. Trahey, “Relative performance of two-dimensional speckle-tracking techniques: normalized correlation, non-normalized correlation and sum-absolute-difference,” in 1995 IEEE Ultrasonics Symposium: Proceedings (IEEE, 1995), Vol. 1482, pp. 1481-1484.

Brown, C. M.

D. H. Ballard and C. M. Brown, Computer Vision (Prentice Hall Professional Technical Reference, 1982).

Cagnoni, S.

G. Adorni, F. Bergenti, and S. Cagnoni, “Vehicle license plate recognition by means of cellular automata,” in Proceedings of the IEEE International Conference on Intelligent Vehicles (IEEE, 1998), pp. 689-693.

Chen, C. N.

C. N. Chen, “Study on wafer identification recognition,” Master's thesis (Tatung University, 2005).

Chen, H. Y.

W. N. Lie, H. C. Hsieh, C. L. Wu, and H. Y. Chen, “A template-matching-based wafer ID recognition system,” in Proceedings of the 1999 Workshop on Computer Vision, Graphics and Image Processing (CVGIP'99, 1999), pp. 722-727.

Cho, D. U.

D. U. Cho and Y. H. Cho, “Implementation of preprocessing independent of environment and recognition of car number plate using histogram and template matching,” J. Korean Inst. Commun. Sci. 23, 94-100 (1998).

Cho, Y. H.

D. U. Cho and Y. H. Cho, “Implementation of preprocessing independent of environment and recognition of car number plate using histogram and template matching,” J. Korean Inst. Commun. Sci. 23, 94-100 (1998).

Chuang, C.

C. Chuang and L. Tseng, “A stroke extraction method for multifont Chinese characters basedon the reduced special interval graph,” IEEE Trans. Syst. Man Cybern. 25, 1171-1178 (1995).
[CrossRef]

Cui, Y. T.

Y. T. Cui and Q. Huang, “Character extraction of license plates from video,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 502-507.
[CrossRef]

Da-Shan, G.

G. Da-Shan and Z. Jie, “Car license plates detection from complex scene,” in 2000 5th International Conference on Signal Processing: Proceedings (IEEE, 2000), pp. 1409-1414.
[CrossRef]

de la Haye, R. J.

H. A. Hegt, R. J. de la Haye, and N. A. Khan, “A high performance license plate recognition system,” in IEEE International Conference on Systems, Man, and Cybernetics (IEEE, 1998), pp. 4357-4362.

Deb, K.

K. Deb, S. Kang, and K. Jo, “Statistical characteristics in HSI color model and position histogram based vehicle license plate detection,” Intell. Serv. Robotics 2, 173-186(2009).
[CrossRef]

Di Claudio, E. D.

R. Parisi, E. D. Di Claudio, G. Lucarelli, and G. Orlandi, “Car plate recognition by neural networks and image processing,” in ISCAS '98, Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (IEEE, 1998), pp. 195-198.
[CrossRef]

Fan, L.

L. Fan and C. L. Tan, “Binarizing document image using coplanar prefilter,” in Sixth International Conference on Document Analysis and Recognition (IEEE, 2001), pp. 34-38.

Faradji, F.

F. Faradji, A. H. Rezaie, and M. Ziaratban, “A morphological-based license plate location,” in IEEE International Conference on Image Processing (IEEE, 2007), pp. I-57-I-60.

Friemel, B. H.

B. H. Friemel, L. N. Bohs, and G. E. Trahey, “Relative performance of two-dimensional speckle-tracking techniques: normalized correlation, non-normalized correlation and sum-absolute-difference,” in 1995 IEEE Ultrasonics Symposium: Proceedings (IEEE, 1995), Vol. 1482, pp. 1481-1484.

Garcia, N.

L. Salgado, J. M. Menendez, E. Rendon, and N. Garcia, “Automatic car plate detection and recognition through intelligent vision engineering,” in 1999 IEEE International Carnahan Conference on Security Technology (IEEE, 1999), pp. 71-76.

Ghadiali, M.

J. C. H. Poon, M. Ghadiali, G. M. T. Mao, and L. M. Sheung, “A robust vision system for vehicle licence plate recognition using grey-scale morphology,” in Proceedings of the IEEE International Symposium on Industrial Electronics (IEEE, 1995), pp. 394-399.
[CrossRef]

Hanmandlu, M.

M. Hanmandlu and O. Murthy, “Fuzzy model based recognition of handwritten numerals,” Pattern Recognition 40, 1840-1854 (2007).
[CrossRef]

Hegt, H. A.

H. A. Hegt, R. J. de la Haye, and N. A. Khan, “A high performance license plate recognition system,” in IEEE International Conference on Systems, Man, and Cybernetics (IEEE, 1998), pp. 4357-4362.

Helmholt, K. A.

J. A. G. Nijhuis, M. H. Ter Brugge, K. A. Helmholt, J. P. W. Pluim, L. Spaanenburg, R. S. Venema, and M. A. Westenberg, “Car license plate recognition with neural networks and fuzzy logic,” in 1995 IEEE International Conference on Neural Networks: Proceedings (IEEE, 1995), pp. 2232-2236.
[CrossRef]

Houle, G.

M. Shridhar, J. W. V. Miller, G. Houle, and L. Bijnagte, “Recognition of license plate images: issues and perspectives,” in Proceedings of the Fifth International Conference on Document Analysis and Recognition (IEEE, 1999), pp. 17-20.

Hsieh, H. C.

W. N. Lie, H. C. Hsieh, C. L. Wu, and H. Y. Chen, “A template-matching-based wafer ID recognition system,” in Proceedings of the 1999 Workshop on Computer Vision, Graphics and Image Processing (CVGIP'99, 1999), pp. 722-727.

Huang, Q.

Y. T. Cui and Q. Huang, “Character extraction of license plates from video,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 502-507.
[CrossRef]

Huang, X.

W. Wen, X. Huang, L. Yang, Z. Yang, and P. Zhang, “Vehicle license plate location method based-on wavelet transform,” in Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization (IEEE, 2009), pp. 381-384.
[CrossRef] [PubMed]

Jie, Z.

G. Da-Shan and Z. Jie, “Car license plates detection from complex scene,” in 2000 5th International Conference on Signal Processing: Proceedings (IEEE, 2000), pp. 1409-1414.
[CrossRef]

Jo, K.

K. Deb, S. Kang, and K. Jo, “Statistical characteristics in HSI color model and position histogram based vehicle license plate detection,” Intell. Serv. Robotics 2, 173-186(2009).
[CrossRef]

Kang, S.

K. Deb, S. Kang, and K. Jo, “Statistical characteristics in HSI color model and position histogram based vehicle license plate detection,” Intell. Serv. Robotics 2, 173-186(2009).
[CrossRef]

Khan, N. A.

H. A. Hegt, R. J. de la Haye, and N. A. Khan, “A high performance license plate recognition system,” in IEEE International Conference on Systems, Man, and Cybernetics (IEEE, 1998), pp. 4357-4362.

Kim, D. W.

S. K. Kim, D. W. Kim, and H. J. Kim, “A recognition of vehicle license plate using a genetic algorithmbased segmentation,” in 1996 International Conference on Image Processing (IEEE, 1996), pp. 661-664.

Kim, H. J.

S. K. Kim, D. W. Kim, and H. J. Kim, “A recognition of vehicle license plate using a genetic algorithmbased segmentation,” in 1996 International Conference on Image Processing (IEEE, 1996), pp. 661-664.

K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Neural Networks for Signal Processing X: Proceedings of the 2000 IEEE Signal Processing Society Workshop (IEEE, 2000), pp. 614-623.
[CrossRef] [PubMed]

Kim, J. B.

K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Neural Networks for Signal Processing X: Proceedings of the 2000 IEEE Signal Processing Society Workshop (IEEE, 2000), pp. 614-623.
[CrossRef] [PubMed]

Kim, K. I.

K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Neural Networks for Signal Processing X: Proceedings of the 2000 IEEE Signal Processing Society Workshop (IEEE, 2000), pp. 614-623.
[CrossRef] [PubMed]

Kim, K. K.

K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Neural Networks for Signal Processing X: Proceedings of the 2000 IEEE Signal Processing Society Workshop (IEEE, 2000), pp. 614-623.
[CrossRef] [PubMed]

Kim, K. M.

K. M. Kim, B. J. Lee, K. Lyou, and G. T. Park, “The automatic recognition of the plate of vehicle using the correlation coefficient and Hough transform,” J. Contr. Autom. Syst. Eng. 3, 511-519 (1997).

Kim, S. K.

S. K. Kim, D. W. Kim, and H. J. Kim, “A recognition of vehicle license plate using a genetic algorithmbased segmentation,” in 1996 International Conference on Image Processing (IEEE, 1996), pp. 661-664.

Kise, K.

N. Babaguchi, K. Yamada, K. Kise, and Y. Tezuka, “Connectionist model binarization,” in 10th International Conference on Pattern Recognition (IEEE, 1990), pp. 51-56.
[CrossRef]

Komori, K.

A. Shio and K. Komori, “A stroke extraction method for handprinted Chinese character recognition,” IECE Trans. IE80-14, 83-90 (1980).

Lee, B. J.

K. M. Kim, B. J. Lee, K. Lyou, and G. T. Park, “The automatic recognition of the plate of vehicle using the correlation coefficient and Hough transform,” J. Contr. Autom. Syst. Eng. 3, 511-519 (1997).

Lehal, G. S.

G. S. Lehal, “Optical character recognition of Gurmukhi script using multiple classifiers,” in Proceedings of the International Workshop on Multilingual OCR (ACM, 2009), pp. 1-9.
[CrossRef]

Lie, W. N.

W. N. Lie, H. C. Hsieh, C. L. Wu, and H. Y. Chen, “A template-matching-based wafer ID recognition system,” in Proceedings of the 1999 Workshop on Computer Vision, Graphics and Image Processing (CVGIP'99, 1999), pp. 722-727.

Lin, T. L.

T. L. Lin, “Intelligent license plate searching and content segmentation in image processing,” Master thesis (National Taiwan University, 2000).

Liu, Y.

Y. Liu and S. N. Srihari, “Document image binarization based on texture features,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 540-544 (1997).
[CrossRef]

Lucarelli, G.

R. Parisi, E. D. Di Claudio, G. Lucarelli, and G. Orlandi, “Car plate recognition by neural networks and image processing,” in ISCAS '98, Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (IEEE, 1998), pp. 195-198.
[CrossRef]

Lyou, K.

K. M. Kim, B. J. Lee, K. Lyou, and G. T. Park, “The automatic recognition of the plate of vehicle using the correlation coefficient and Hough transform,” J. Contr. Autom. Syst. Eng. 3, 511-519 (1997).

Mahmoud, A.

S. Mahmoud and A. Mahmoud, “Arabic character recognition using modified Fourier spectrum (MFS) vs. Fourier descriptors,” Cybern. Syst. 40, 189-210 (2009).
[CrossRef]

Mahmoud, S.

S. Mahmoud and A. Mahmoud, “Arabic character recognition using modified Fourier spectrum (MFS) vs. Fourier descriptors,” Cybern. Syst. 40, 189-210 (2009).
[CrossRef]

Mao, G. M. T.

J. C. H. Poon, M. Ghadiali, G. M. T. Mao, and L. M. Sheung, “A robust vision system for vehicle licence plate recognition using grey-scale morphology,” in Proceedings of the IEEE International Symposium on Industrial Electronics (IEEE, 1995), pp. 394-399.
[CrossRef]

Menendez, J. M.

L. Salgado, J. M. Menendez, E. Rendon, and N. Garcia, “Automatic car plate detection and recognition through intelligent vision engineering,” in 1999 IEEE International Carnahan Conference on Security Technology (IEEE, 1999), pp. 71-76.

Miller, J. W. V.

M. Shridhar, J. W. V. Miller, G. Houle, and L. Bijnagte, “Recognition of license plate images: issues and perspectives,” in Proceedings of the Fifth International Conference on Document Analysis and Recognition (IEEE, 1999), pp. 17-20.

Murthy, O.

M. Hanmandlu and O. Murthy, “Fuzzy model based recognition of handwritten numerals,” Pattern Recognition 40, 1840-1854 (2007).
[CrossRef]

Nijhuis, J. A. G.

J. A. G. Nijhuis, M. H. Ter Brugge, K. A. Helmholt, J. P. W. Pluim, L. Spaanenburg, R. S. Venema, and M. A. Westenberg, “Car license plate recognition with neural networks and fuzzy logic,” in 1995 IEEE International Conference on Neural Networks: Proceedings (IEEE, 1995), pp. 2232-2236.
[CrossRef]

M. H. ter Brugge, J. H. Stevens, J. A. G. Nijhuis, and L. Spaanenburg, “License plate recognition using DTCNNs,” in 1998 Fifth IEEE International Workshop on Cellular Neural Networks and Their Applications: Proceedings (IEEE, 1998), pp. 212-217.
[CrossRef]

Orlandi, G.

R. Parisi, E. D. Di Claudio, G. Lucarelli, and G. Orlandi, “Car plate recognition by neural networks and image processing,” in ISCAS '98, Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (IEEE, 1998), pp. 195-198.
[CrossRef]

Parisi, R.

R. Parisi, E. D. Di Claudio, G. Lucarelli, and G. Orlandi, “Car plate recognition by neural networks and image processing,” in ISCAS '98, Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (IEEE, 1998), pp. 195-198.
[CrossRef]

Park, G. T.

K. M. Kim, B. J. Lee, K. Lyou, and G. T. Park, “The automatic recognition of the plate of vehicle using the correlation coefficient and Hough transform,” J. Contr. Autom. Syst. Eng. 3, 511-519 (1997).

Pluim, J. P. W.

J. A. G. Nijhuis, M. H. Ter Brugge, K. A. Helmholt, J. P. W. Pluim, L. Spaanenburg, R. S. Venema, and M. A. Westenberg, “Car license plate recognition with neural networks and fuzzy logic,” in 1995 IEEE International Conference on Neural Networks: Proceedings (IEEE, 1995), pp. 2232-2236.
[CrossRef]

Poon, J. C. H.

J. C. H. Poon, M. Ghadiali, G. M. T. Mao, and L. M. Sheung, “A robust vision system for vehicle licence plate recognition using grey-scale morphology,” in Proceedings of the IEEE International Symposium on Industrial Electronics (IEEE, 1995), pp. 394-399.
[CrossRef]

Rendon, E.

L. Salgado, J. M. Menendez, E. Rendon, and N. Garcia, “Automatic car plate detection and recognition through intelligent vision engineering,” in 1999 IEEE International Carnahan Conference on Security Technology (IEEE, 1999), pp. 71-76.

Rezaie, A. H.

F. Faradji, A. H. Rezaie, and M. Ziaratban, “A morphological-based license plate location,” in IEEE International Conference on Image Processing (IEEE, 2007), pp. I-57-I-60.

Salgado, L.

L. Salgado, J. M. Menendez, E. Rendon, and N. Garcia, “Automatic car plate detection and recognition through intelligent vision engineering,” in 1999 IEEE International Carnahan Conference on Security Technology (IEEE, 1999), pp. 71-76.

Sheung, L. M.

J. C. H. Poon, M. Ghadiali, G. M. T. Mao, and L. M. Sheung, “A robust vision system for vehicle licence plate recognition using grey-scale morphology,” in Proceedings of the IEEE International Symposium on Industrial Electronics (IEEE, 1995), pp. 394-399.
[CrossRef]

Shio, A.

A. Shio and K. Komori, “A stroke extraction method for handprinted Chinese character recognition,” IECE Trans. IE80-14, 83-90 (1980).

Shridhar, M.

M. Shridhar, J. W. V. Miller, G. Houle, and L. Bijnagte, “Recognition of license plate images: issues and perspectives,” in Proceedings of the Fifth International Conference on Document Analysis and Recognition (IEEE, 1999), pp. 17-20.

Spaanenburg, L.

J. A. G. Nijhuis, M. H. Ter Brugge, K. A. Helmholt, J. P. W. Pluim, L. Spaanenburg, R. S. Venema, and M. A. Westenberg, “Car license plate recognition with neural networks and fuzzy logic,” in 1995 IEEE International Conference on Neural Networks: Proceedings (IEEE, 1995), pp. 2232-2236.
[CrossRef]

M. H. ter Brugge, J. H. Stevens, J. A. G. Nijhuis, and L. Spaanenburg, “License plate recognition using DTCNNs,” in 1998 Fifth IEEE International Workshop on Cellular Neural Networks and Their Applications: Proceedings (IEEE, 1998), pp. 212-217.
[CrossRef]

Srihari, S. N.

Y. Liu and S. N. Srihari, “Document image binarization based on texture features,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 540-544 (1997).
[CrossRef]

Stevens, J. H.

M. H. ter Brugge, J. H. Stevens, J. A. G. Nijhuis, and L. Spaanenburg, “License plate recognition using DTCNNs,” in 1998 Fifth IEEE International Workshop on Cellular Neural Networks and Their Applications: Proceedings (IEEE, 1998), pp. 212-217.
[CrossRef]

Tan, C. L.

L. Fan and C. L. Tan, “Binarizing document image using coplanar prefilter,” in Sixth International Conference on Document Analysis and Recognition (IEEE, 2001), pp. 34-38.

Ter Brugge, M. H.

J. A. G. Nijhuis, M. H. Ter Brugge, K. A. Helmholt, J. P. W. Pluim, L. Spaanenburg, R. S. Venema, and M. A. Westenberg, “Car license plate recognition with neural networks and fuzzy logic,” in 1995 IEEE International Conference on Neural Networks: Proceedings (IEEE, 1995), pp. 2232-2236.
[CrossRef]

M. H. ter Brugge, J. H. Stevens, J. A. G. Nijhuis, and L. Spaanenburg, “License plate recognition using DTCNNs,” in 1998 Fifth IEEE International Workshop on Cellular Neural Networks and Their Applications: Proceedings (IEEE, 1998), pp. 212-217.
[CrossRef]

Tezuka, Y.

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Figures (19)

Fig. 1
Fig. 1

Flow diagram of a typical OCR approach.

Fig. 2
Fig. 2

(a) Disturbances caused by uneven luminance causes. (b) Disturbances caused by wafer scribe processes. (c) Unsuccessive horizontal wafer scribes in vertical direction.

Fig. 3
Fig. 3

Flow diagram of the proposed approach.

Fig. 4
Fig. 4

(a) Original wafer ID image with interference arcs. (b) Result after the first operation of masking according to Eqs. (1, 2, 3, 4). (c) Result for the second time masking operation the same procedure as (b). (d) Result for the third time masking operation same as (b).

Fig. 5
Fig. 5

(a) Horizontal projection histogram of the binarized differential image of Fig. 4d. (b) Localization results according to the horizontal projection.

Fig. 6
Fig. 6

(a) Cut wafer ID subimage after locating procedure. (b) Gray-level histogram and the accumulated ratio curve L.

Fig. 7
Fig. 7

(a) Original image before enhancement. (b) Resulting image after enhancement.

Fig. 8
Fig. 8

Flow chart of the image binarization.

Fig. 9
Fig. 9

(a) Four distribution orientations of the asterisk-shape filter window: (b) upper detective region, (c) lower detective region, (d) left detective region, (e) right detective region.

Fig. 10
Fig. 10

(a) Original image. (b) Resulting image after asterisk-shape filtering. (c) Processing result of the high–low score comparison method. (d) Processing result that combined by (b) and (c) and determined by the characteristic of wafer scribe.

Fig. 11
Fig. 11

(a) The closer the scribe center, the more outstanding the gray levels. The arrows indicate the scribe center. (b) Histogram of (a). The locations near to the scribe center have peaks.

Fig. 12
Fig. 12

(a) Wafer scribe edges are unsuccessive in the original image. (b) In the processed image, the wafer scribe unsuccessive edges come out clearly. (c) Processing result shows the vertical edges are compensated in the processed image.

Fig. 13
Fig. 13

(a) Original image. (b) Approximate location results from horizontal projection of the binarized image. (c) Processing result of skew angle correction for mean square transformation.

Fig. 14
Fig. 14

Process of finding the character centers by analyzing the histogram of vertical projection.

Fig. 15
Fig. 15

Process segments out characters for the noisy images in Fig. 2.

Fig. 16
Fig. 16

Ten detective regions for stroke analysis: (a) vertical detective regions, (b) horizontal detective regions, (c) left top detective region and right bottom detective region, (d) right top detective region and left bottom detective region.

Fig. 17
Fig. 17

Set of standard characters are collected as standard patterns.

Fig. 18
Fig. 18

Unsuccessful examples for wrong location, characters cut out from the image, skew angle correction fails, and character segmentation fails.

Fig. 19
Fig. 19

Unsuccessful examples for character recognition fail.

Tables (2)

Tables Icon

Table 1 Experiment Results of the Proposed Approach

Tables Icon

Table 2 Wrong Matching List

Equations (21)

Equations on this page are rendered with MathJax. Learn more.

g 1 ( x , y ) = f ( x , y ) H ( i ) = i = 0 3 f ( x + i , y ) · H ( i ) ,
g 2 ( x , y ) = g 1 ( x , y ) B ( i ) = i = 0 3 g 1 ( x + i , y ) · B ( i ) ,
g 3 ( x , y ) = { 255 if    | g 2 ( x , y ) g 2 ( x 3 , y ) | δ 1 0 otherwise ,
g 4 ( x , y ) = { 255 for    k = 1 1 g 3 ( x , y + k ) 510 0 otherwise ,
r = sr + br 2 ,
f ( x ) = r + δ 1 sr br ( x r ) .
flag right = { true if     k = 1 4 f ( x + k , y ) f ( x , y ) < δ 4 or k = 1 4 f ( x + k , y ) = 0 false otherwise ,
flag upper _ right = { true if     k = 1 4 f ( x + k , y + k ) f ( x , y ) < δ 4 or k = 1 4 f ( x + k , y + k ) = 0 false otherwise ,
flag upper = { true if     k = 1 4 f ( x , y + k ) f ( x , y ) < δ 4 or k = 1 4 f ( x , y + k ) = 0 false otherwise ,
flag upper _ left = { true if     k = 1 4 f ( x k , y + k ) f ( x , y ) < δ 4 or k = 1 4 f ( x k , y + k ) = 0 false otherwise ,
flag left = { true if     k = 1 4 f ( x k , y ) f ( x , y ) < δ 4 or k = 1 4 f ( x k , y ) = 0 false otherwise ,
flag lower _ left = { true if     k = 1 4 f ( x k , y k ) f ( x , y ) < δ 4 or k = 1 4 f ( x k , y k ) = 0 false otherwise ,
flag lower = { true if     k = 1 4 f ( x , y k ) f ( x , y ) < δ 4 or k = 1 4 f ( x , y k ) = 0 false otherwise ,
flag lower _ right = { true if     k = 1 4 f ( x + k , y k ) f ( x , y ) < δ 4 or k = 1 4 f ( x + k , y k ) = 0 false otherwise ,
δ 4 = { δ 6 if     sr br δ 2 and sr br δ 5 δ 7 if     sr br < δ 2 or sr br > δ 5 ,
H ( x , y ) = 0 if     G ( x , y ) = 0 ,
HighScore avg = Average ( i = 4 4 j = 4 4 f ( x + i , y + j ) ) if     G ( x , y ) < i = 4 4 j = 4 4 f ( x + i , y + j ) and G ( x , y ) 0 ,
LowScore avg = Average ( i = 4 4 j = 4 4 f ( x + i , y + j ) ) if     G ( x , y ) > i = 4 4 j = 4 4 f ( x + i , y + j ) and G ( x , y ) 0 ,
H ( x , y ) = { 255 if    G ( x , y ) > LowScore avg + δ 8 and G ( x , y ) > ( LowScore avg + HighScore avg ) / 2 0 otherwise ,
g 5 ( i , j ) = { 255 if    k = 2 2 T ( i , j + k ) 765 0 otherwise ,
r = N i = 1 N x i y i i = 1 N x i i = 1 N y i [ N i = 1 N x i 2 ( i = 1 N x i ) 2 ] · [ N i = 1 N y i 2 ( i = 1 N y i ) 2 ] ,

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