Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Detection of Text Candidate Regions using Region Information-based Genetic Algorithm

영역정보기반의 유전자알고리즘을 이용한 텍스트 후보영역 검출

  • Oh, Jun-Taek (School of Electrical Engineering and Computer Science, Yeungnam University) ;
  • Kim, Wook-Hyun (School of Electrical Engineering and Computer Science, Yeungnam University)
  • 오준택 (영남대학교 전자정보공학부) ;
  • 김욱현 (영남대학교 전자정보공학부)
  • Published : 2008.11.25
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Abstract

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

본 논문은 화소 단위의 정보가 아닌 분할된 영역들의 정보를 기반으로 유전자 알고리즘을 이용한 텍스트 후보영역 검출방안을 제안한다. 먼저, 영상분할을 수행하기 위해 색상별 화소분류와 비동질적인 군집의 감소를 위한 영역 단위의 재분류 알고리즘을 수행한다. 색상별 화소분류에 이용되는 EWFCM(Entropy-based Weighted Fuzzy C-Means) 알고리즘은 공간정보를 추가한 개선된 FCM 알고리즘으로써, 잡음에 강건한 특징을 가진다. EWFCM 알고리즘에 의해 분류된 화소들의 군집정보를 기반으로 수행되는 영역 단위의 재분류는 화소나 군집 단위의 재분류에 비해 효과적으로 영상에 존재하는 비동질적인 군집들을 감소시킬 수 있다. 그리고 텍스트 후보영역 검출은 분할된 영역들로부터 추출한 방향성 에지 성분에 대한 분산값 및 에너지, 크기, 개수 등의 정보를 기반으로 유전자알고리즘에 의해 수행된다. 이는 화소 단위의 정보를 이용한 방법보다 더 명확한 텍스트 영역정보를 획득할 수 있으며, 향후 자동문자인식에서 좀 더 손쉽게 이용될 수 있다. 실험 결과 제안한 분할방법은 기존 방법이나 화소나 군집 기반의 재분류보다 좋은 결과를 보였으며, 텍스트 후보영역 검출에서도 화소 단위의 정보를 이용한 기존 방법보다 더 좋은 결과를 보여 제안방법의 유효성을 확인하였다.

Keywords

References

  1. C. Liu, C. Wang, and R. Dai, "Text detection in images based on unsupervised classification of edge-based features," Proc. of International Conf. on Document Analysis and Recognition, vol.2, pp.610-614, 2005
  2. D. Chen, K. Shearer, and H. Bourlard, "Text enhancement with asymmetric filter for video OCR," Proc. of International Conf. on Image Analysis and Processing, pp.192-197, 2001
  3. X. Liu and J. Samarabandu, "An edge-based text region extraction algorithm for indoor mobile robot navigation," Proc. of IEEE Conf. on Mechatronics and Automation, vol.2, p.701-706, 2005
  4. X.-S. Hua, L. Wenyin, H.-J. Zhang, "An automatic performance evaluation protocol for video text detection algorithms," IEEE Trans. on Circuits and Systems for Video Technology, vol.14, no.4, pp.498-507, 2004 https://doi.org/10.1109/TCSVT.2004.825538
  5. H. Li, D. Doermann, and O. Kia, "Automatic text detection and tracking in digital video," IEEE Trans. on Image Processing, vol.9, no.1, pp.147-156, 2000 https://doi.org/10.1109/83.817607
  6. K. I. Kim, K. Jung, and J. H. Kim, "Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.25, no.12, pp.1631-1639, 2003 https://doi.org/10.1109/TPAMI.2003.1251157
  7. K.-Y. Jeong, K. Jung, E. Y. Kim, and H. J. Kim, "Neural network-based text location for news video indexing," Proc. of International Conf. on Image Processing, vol.3, pp.319-323, 1999
  8. J. T. Oh, H. W. Kwak, Y. H. Sohn, and W. H. Kim, "Multi-level thresholding using entropy- based weighted FCM algorithm in color image," LNCS 3804, pp.437-444, 2005
  9. U. Maulik and S. Bandyopadhyay, "Genetic Algorithm-based Clustering Technique," Pattern Recognition, vol.33, no.9, pp.1455-1465, 2000 https://doi.org/10.1016/S0031-3203(99)00137-5
  10. M. Borsotti, P. Campadelli and R. Schettini, "Quantitative evaluation of color image segmentation results," Patt. Recogn. Lett. vol.19, no.8, pp.741-747, 1998 https://doi.org/10.1016/S0167-8655(98)00052-X
  11. Y. Du, C. Chang, and P. D. Thouin, "Unsupervised approach to color video thresholding," Opt. Eng. vol.32, no.2, pp.282-289, 2004
  12. Y. Du, C. I. Change and P. D. Thouin, "An unsupervised approach to color video thresholding," Proc. of IEEE Conf. on Acoustics, Speech and Signal Processing, vol.3, pp.373-376, 2003
  13. N. Otsu, "A threshold selection method from gray level histograms," IEEE Trans. Syst. Man Cybern. vol.9, no.1, pp.62-66, 1979 https://doi.org/10.1109/TSMC.1979.4310076