A neural-based page segmentation system

Journal of Circuits, Systems and Computers

Volumn 14, Issue 1, 2005, Pages 109-122

  Alginahi, Y ^a   Fekri, D ^a   Sid Ahmed, M A ^a  

^a UNIVERSITY OF WINDSOR   (Canada)

Author keywords

Linear classification; Multi layer perceptron; Neural networks; Page segmentation; Skew detection

Indexed keywords

ALGORITHMS; COMPUTER GRAPHICS; DATA ACQUISITION; IMAGE ANALYSIS; MULTILAYER NEURAL NETWORKS; OPTICAL CHARACTER RECOGNITION;

LINEAR CLASSIFICATION; PAGE LAYOUT ANALYSIS; PAGE SEGMENTATION; SKEW DETECTION;

IMAGE SEGMENTATION;

EID: 17444371301     PISSN: 02181266     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218126605002192     Document Type: Article

References (23)

1. F. M. Wahl, K. Y. Wong and R. G. Casey, Block segmentation and text extraction in mixed text/image documents, Comput. Graphics Image Process. 20 (1982) 375-390.
2. F. M. Wahl, A new distance mapping and its use for shape measurement on binary patterns, Comput. Vision, Graphics, Image Process. 23 (1983) 218-226.
3. S. C. Hinds, J. L. Fisher and D. P. D'Amato, A document skew detection method using run length encoding and Hough transform, Proc. IEEE 10th Int. Conf. Pattern Recognition (1990), pp. 464-468.
4. L. O'Gorman, The document spectrum for page layout analysis, IEEE Trans. Pattern Anal. Machine Intell. 15 (1993) 1162-1173.
5. D. S. Le, G. R. Thomas and H. Wechsler, Automated page orientation and skew angle detection of binary document images, Pattern Recogn. 27 (1994) 1325-1344.
6. W. Postl, Detection of linear oblique structures and skew scan in digitized documents, Proc. 8th Int. Conf. Pattern Recognition (1986), pp. 687-689.
7. A. Amin and S. Fischer, A document skew detection method using the Hough transform, Pattern Anal. Appl. 3 (2000) 243-253.
8. http://homepages.borland.com/efg21ab/ImageProcessing/RotateScanline.htm.
9. L. Abele, F. Wahl and W. Scherl, Procedures for an automatic segmentation of text, graphics and half tone regions in documents, Proc. 2nd Scandinavian Conf. Image Analysis, Helsinki (1981), pp. 177-182.
10. W. Doster, Different states of a documents content on its way from the Guttenbergian world to the electronic world, Proc. ICPR, Vol. 2, Montreal (1984), pp. 872-874.
11. S. Nagy and S. Stoddard, Document analysis with an expert system, Pattern Recogn. Practice II (1986) 149-155.
12. D. Drivas and A. Amin, Page segmentation and classification utilizing bottom-up approach, Proc. IEEE 3rd Int. Conf. Document Analysis and Recognition, Los Alamitos, USA (1995), pp. 610-614.
13. K. Wong, R. Casey and F. Wahl, Document analysis system, IBM J. Res. Devel. 26 (1982) 647-656.
14. N. K. Ratha and A. K. Jain, Multi-layer perceptrons on Splash 2, Proc. Fourth IEEE Int. Workshop on Computer Architecture for Machine Perception (1997), pp. 138-142.
15. A. K. Jain and Y. Zhong, Page segmentation using texture discrimination masks, Proc. Int. Conf. Image Processing, Vol. 3 (1995), pp. 308-311.
16. S. K. Yip and Z. Chi, Page segmentation and content classification for automatic document image processing, Proc. Int. Symp. Intelligent Multimedia, Video and Speech Processing (2001), pp. 279-282.
17. Z. Chi and J. Kong, Image content classification using a block Kolmogorov complexity measure, Proc. ICSP, Vol. 2 (1998), pp. 1185-1188.
18. C. Strouthopoulos, N. Papamarkos and A. E. Atsalakis, Text extraction in complex color documents, Pattern Recogn. 35 (2002) 1743-1758.
19. A. K. Jain and Y. Zhong, Page segmentation using texture analysis, Pattern Recogn. 29 (1996) 743-770.
20. M. A. Sid-Ahmed, Image Processing: Theory, Algorithms, and Architectures (McGraw-Hill, New York, 1995).
21. C. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1995).
22. R. P. Lippmann, An introduction to computing with neural networks, IEEE ASSP Mag. 4 (1987) 4-21.
23. D. E. Rumelhart, G. E. Hinton and R. J. Williams, Learning internal representations by error propagation, Parallel Distributed Processing. Explorations in the Microstructures of Cognition, Vol. 1, Foundations, eds. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group (MIT Press, Cambridge, MA, 1988), pp. 318-362.