Active Contour based Automatic Feedback for Optical Character Recognition

Joanna Isabelle Olszewska

2014

Abstract

In this paper, we present a new optical character recognition approach. Our method combines chromaticitybased character detection with active contour segmentation in order to robustly extract optical characters from real-world images and videos. The detected character is recognized using template matching. Our developed approach has shown excellent results when applied to the automatic identification of team players from online datasets and is more efficient than the state-of-the-art methods.

References

  1. Alqaisi, T., Gledhill, D., and Olszewska, J. I. (2012). Embedded double matching of local descriptors for a fast automatic recognition of real-world objects. In Proceedings of the IEEE International Conference on Image Processing (ICIP'12), pages 2385-2388.
  2. Alsuqayhi, A. and Olszewska, J. I. (2013). Embedded double matching of local descriptors for a fast automatic recognition of real-world objects. In Proceedings of the IAPR International Conference on Computer Analysis of Images and Patterns Workshop (CAIP'13), pages 139-150.
  3. Andrade, E. L., Khan, E., Woods, J. C., and Ghanbari, M. (2003). Player classification in interactive sport scenes using prior information region space analysis and number recognition. In Proceedings of the IEEE International Conference on Image Processing (ICIP'03), pages III.129-III.132.
  4. Bertini, M., Bimbo, A. D., and Nunziati, W. (2006). Matching faces with textual cues in soccer videos. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 537-540.
  5. Brunelli, R. (2009). Template Matching Techniques in Computer Vision: Theory and Practice. John Wiley and Sons.
  6. Chen, X. and Yuille, A. L. (2004). Detecting and reading text in natural scenes. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages II.366-II.373.
  7. D'Orazio, T. and Leo, M. (2010). A review of vision-based systems for soccer video analysis. Pattern Recognition, 43(8):2911-2926.
  8. Ekin, A., Tekalp, M., and Mehrotra, R. (2003). Automatic soccer video analysis and summarization. IEEE Transactions on Image Processing, 12(7):796-806.
  9. Guanglin, H. and Yali, G. (2010). A simple and fast method of recognizing license plate number. In Proceedings of the IEEE International Forum on Information Technology and Applications, pages II.23-II.26.
  10. Huang, C.-L., Shih, H.-C., and Chao, C.-Y. (2006). Semantic analysis of soccer video using dynamic Bayesian network. IEEE Transactions on Multimedia, 8(4):749-760.
  11. Kokaram, A., Rea, N., Dahyot, R., Tekalp, A. M., Bouthemy, P., Gros, P., and Sezan, I. (2006). Browsing sports video: Trends in sports-related indexing and retrieval work. IEEE Signal Processing Magazine, 23(2):47-58.
  12. Lin, C.-C. and Huang, W.-H. (2007). Locating license plate based on edge features of intensity and saturation. In Proceedings of the IEEE International Conference on Innovative Computing, Information and Control, pages 227-230.
  13. Niu, Z., Gao, X., Tao, D., and Li, X. (2008). Semantic video shot segmentation based on color ratio feature and SVM. In Proceedings of the IEEE International Conference on Cyberworlds, pages 157-162.
  14. Olszewska, J. I. (2011). Spatio-temporal visual ontology. In Proceedings of the EPSRC Workshop on Vision and Language.
  15. Olszewska, J. I. (2012a). A new approach for automatic object labeling. In Proceedings of the EPSRC Workshop on Vision and Language.
  16. Olszewska, J. I. (2012b). Multi-target parametric active contours to support ontological domain representation. In Proceedings of the RFIA Conference, pages 779-784.
  17. Olszewska, J. I. and McCluskey, T. L. (2011). Ontologycoupled active contours for dynamic video scene understanding. In Proceedings of the IEEE International Conference on Intelligent Engineering Systems, pages 369-374.
  18. Ren, M., Yang, J., and Sun, H. (2002). Tracing boundary contours in a binary image. Image and Vision Computing, 20(2):125-131.
  19. Saric, M., Dujmic, H., Papic, V., and Rozic, N. (2008). Player number localization and recognition in soccer video using HSV color space and internal contours. In Proceedings of the World Academy of Science, Engineering and Technology, pages 531-535.
  20. Wood, R. and Olszewska, J. I. (2012). Lighting-variable AdaBoost based-on system for robust face detection. In Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, pages 494-497.
Download


Paper Citation


in Harvard Style

Olszewska J. (2014). Active Contour based Automatic Feedback for Optical Character Recognition . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 318-324. DOI: 10.5220/0004935603180324


in Bibtex Style

@conference{mpbs14,
author={Joanna Isabelle Olszewska},
title={Active Contour based Automatic Feedback for Optical Character Recognition},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2014)},
year={2014},
pages={318-324},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004935603180324},
isbn={978-989-758-011-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2014)
TI - Active Contour based Automatic Feedback for Optical Character Recognition
SN - 978-989-758-011-6
AU - Olszewska J.
PY - 2014
SP - 318
EP - 324
DO - 10.5220/0004935603180324