Document image binarisation using a supervised neural network

International Journal of Neural Systems

Volumn 18, Issue 5, 2008, Pages 405-418

  Khashman, Adnan ^a   Sekeroglu, Boran ^a  

^a NEAR EAST UNIVERSITY   (Turkey)

Author keywords

Binarisation; Document and text processing; Image enhancement; Neural network; Thresholding

Indexed keywords

COST EFFECTIVENESS; DIGITAL ARITHMETIC; DIGITAL IMAGE STORAGE; DIGITAL LIBRARIES; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; INFORMATION RETRIEVAL SYSTEMS; LIBRARIES; NEURAL NETWORKS; TEXT PROCESSING; WORD PROCESSING;

BACKGROUND NOISES; BINARISATION; DEGRADED DOCUMENTS; DIGITAL TECHNOLOGIES; DOCUMENT AND TEXT PROCESSING; DOCUMENT IMAGES; GLOBAL THRESHOLDING; GLOBAL THRESHOLDS; LOCAL ADAPTIVE THRESHOLDING; LOCAL THRESHOLDING; NOVEL METHODS; SCANNED DOCUMENTS; SUPERVISED NEURAL NETWORKS; THRESHOLDING;

IMAGE ENHANCEMENT;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; IMAGE PROCESSING; INFORMATION CENTER; INFORMATION PROCESSING; METHODOLOGY; STANDARD;

ALGORITHMS; ARCHIVES; ARTIFICIAL INTELLIGENCE; AUTOMATIC DATA PROCESSING; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SOFTWARE;

EID: 55849098570     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065708001671     Document Type: Article

References (34)

1. S. K. Suzuki, I. Horiba and N. Sugie, Neural edge enhancer for supervised edge enhancement from noisy images, IEEE Trans. on Pattern Analysis and Machine Intelligence 25 (2003) 1582-1596.
2. Marinai, M. Gori and G. Soda, Artificial neural networks for document analysis and recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence 27 (1) (January 2005) 23-35.
3. Y. Zheng, H. Li and D. Doermann, Machine printed text and handwriting identification in noisy document images, IEEE Trans. on Pattern Analysis and Machine Intelligence 26 (3) (2004) 337-353.
4. E. Kavallieratou and H. Antonopoulou, Cleaning and enhancing historical document images, Lecture Notes in Computer Science, Springer Berlin/Heidelberg, 3708 (2005) 681-688.
5. B. Gatos, I. Pratikakis and S. J. Perantonis, An adaptive binarisation technique for low quality historical documents, Lecture Notes in Computer Science, Springer Berlin/Heidelberg, 3163 (2004) 102-113.
6. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Reading, MA: Addison-Wesley, 1993).
7. J. Kittler and J. Illingworth, Minimum error thresholding, Pattern Recognition 19 (1) (1986) 41-47.
8. O. D. Trier and A. K. Jain, Goal-directed evaluation of binarisation methods, IEEE Trans. on Pattern Analysis and Machine Intelligence 17 (1995) 1191-1201.
9. M. Sezgin and B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging 13 (1) (2004) 146-168.
10. A. Khashman and B. Sekeroglu, Novel thresholding method for document analysis, IEEE International Conference on Industrial Technology (2006) 616-620.
11. N. Otsu, A threshold selection method from graylevel histogram, IEEE Trans. on Systems, Man, and Cybernetics 9 (1979) 62-66.
12. S. Abutaleb, Automatic thresholding of gray-level pictures using two dimensional entropy, Computer Vision, Graphics, and Image Processing 47 (1989) 22-32.
13. J. N. Kapur, P. K. Sahoo and A. K. C. Wong, A new method for gray-level picture thresholding using the entropy of the histogram, Computer Vision, Graphics, and Image Processing 29 (1985) 273-285.
14. W. Niblack, An introduction to digital image processing, Prentice Hall, (1986), 115-116.
15. B. Gatos, I. Pratikakis and S. J. Perantonis, Adaptive degraded document image binarisation, Pattern Recognition, Elsevier Science Inc. New York, 39 (3) (March 2006) 317-327.
16. J. Delon, A. Desolneux, J. L. Lisani and A. B. Petro, A nonparametric approach for histogram segmentation, IEEE Trans. on Image Processing 16 (1) (January 2007) 253-261.
17. E. Gabarra and A. Tabbone, Combining global and local threshold to binarise document of images, Lecture Notes in Computer Science, Springer Berlin/Heidelberg 3523 (2005) 371-378.
18. I. K. Kim, D. W. Jung and R. H. Park, Document image binarisation based on topographic analysis using a water flow mode, Pattern Recognition, Elsevier Science Ltd., 35 (2002) 265-277.
19. M. Egmont-Petersen, D. de Ridder and H. Handels, Image processing with neural networks - a review, Pattern Recognition 35 (2002) 2279-2301.
20. J. L. Hidalgo, S. Espana, M. J. Castro and J. A. Perez, Enhancement and cleaning of handwritten data, by using neural networks, Lecture Notes in Computer Science: Pattern Recognition and Image Analysis 3522 (2005) 376-383.
21. H. Fu and Z. Chi, Combined thresholding and neural network approach for vein pattern extraction from leaf images, IEE Proc. Vis. Image Signal Process. 153 (6) (December 2006) 881-892.
22. Badekas and N. Papamarkos, Optimal combination of document binarisation techniques using a selforganizing map neural network, Engineering Applications of Artificial Intelligence, Pergamon Press, Inc. Tarrytown, NY, USA, 20 (1) (February 2007) 11-24.
23. Y. Yang, K. Summers and M. Turner, A text image enhancement system based on segmentation and classification method, Proceedings of the 1st ACM Workshop on Hardcopy Document Processing, ACM Press New York, NY, USA, (2004), 33-40.
24. B. Majhi and P. K. Sa, FLANN-based adaptive threshold selection for detection of impulsive noise in images, Int. J. Electron. Commun., (AEÚ), doi: 10.1016/j.aeue.2006.08.007. (2006).
25. S. Haykin, Neural networks: A comprehensive foundation, Englewood Cliffs, NJ: Prentice-Hall (1999).
26. Z. Chi and K. W. Wong, A two-stage binarisation approach for document images, Proceedings of International Symposium on Intelligent Multimedia, Video and Speech Processing (May 2001) 275-278.
27. M. Fukumi, Y. Takeuchi, H. Fukumoto, Y. Mitsukura and M. Khalid, Neural network based threshold determination for Malaysia License Plate Character Recognition (PDF), Proceedings of ICMT2005, (2005), 1-5.
28. N. Papamarkos, C. Strouthopoulos and I. Andreadis, Multithresholding of color and gray-level images through a neural network technique, Elsevier Science B.V., Image and Vision Computing 18 (2000) 213-222.
29. J. Sauvola and M. Pietikainen, Adaptive document image binarisation, Pattern. Recognition 33 (2000) 225-336.
30. G. Leedham et al., Comparison of some thresholding algorithms for text/background segmentation in difficult document image, Proceedings of Seventh International Conference on Document Analysis and Recognition (2003), 859-864.
31. J. Bernsen, Dynamic thresholding of gray level images, Proceedings of International Conference on Pattern Recognition, (1986), 1251-1255.
32. A. Khashman and B. Sekeroglu, Enhancement of unclear patterns using pattern average thresholding, Proceedings of the 3rd International Symposium on Electrical. Electronic & Computer Engineering 1 (2006) 253-257.
33. X. Ye, M. Cheriet and C. Y. Suen, Stroke-modelbased character extraction from gray-level document images, IEEE Trans. on Image Processing 10 (8) (August 2001) 1152-1161.
34. J. R. Parker, Gray level Thresholding in Badly Illuminated Images, IEEE Trans. on Pattern Analysis and Machine Intelligence 13 (8) (1991) 813-819.