Neural networks for document image preprocessing: state of the art

• Arica N, Yarman-Vural FT (2001) An overview of character recognition focused on off-line handwriting. IEEE Trans Syst Man Cybernet Part C Appl Rev 31(2): 216–233

  Article  Google Scholar 

• Arvind KR, Kumar J, Ramakrishnan AG (2007) Line removal and restoration of handwritten strokes. In: International conference on computational intelligence and multimedia applications, pp 208–214

• Arvind KR, Pati PB, Ramakrishnan AG (2006) Automatic text block separation in document images. In: Proceedings of 4th international conference on intelligent sensing and information processing, pp 53–58

• Bai Z-L, Huo Q (2004) Underline detection and removal in a document image using multiple strategies. In: Proceedings of the 17th international conference on pattern recognition (ICPR04), vol 2, pp 578–581

• Bekhti S, Rehman A, Al-Harbi M, Saba T (2011) AQuASys an Arabic question-answering system based on extensive question analysis and answer relevance scoring. Inf Comput Int J Acad Res 3(4): 45–54

  Google Scholar 

• Belaid Y, Belaid A, Turolla E (1995) Item searching in forms: application to French tax forms. In: Proceedings of 3rd international conference on document analysis and recognition, pp 744–747

• Blumenstein M, Cheng CK, Liu XY (2002) New preprocessing techniques for handwritten word recognition. In: Proceedings of second international conference on visualization, imaging and image processing, ACTA. Press, Calgary, pp 480–484

• Blumenstein M, Liu XY, Verma B (2004) A modified direction feature for cursive character recognition. In: Proceedings of the international joint conference on neural networks, Budapest, Hungary, pp 2983–2989

• Blumenstein M, Liu XY, Verma B (2007) An investigation of the modified direction feature for cursive character recognition. Pattern Recognit 40: 376–388

  Article  MATH  Google Scholar 

• Bozinovic RM, Srihari SN (1989) Off-line cursive script word recognition. IEEE Trans Pattern Anal Mach Intell 11(1): 68–83

  Article  Google Scholar 

• Britto AS, Sabourin JR, Latherier E, Bortolozzi F, Suen CY (2000) Improvement in handwritten numeral string recognition by slant normalization and contextual information. In: Proceeding of seventh international workshop on frontiers in handwriting recognition, pp 323–332

• Cai J, Liu Z-Q (2000) Off-line unconstrained handwritten word recognition. Int J Pattern Recognit Artif Intell 14(3): 259–280

  Article  Google Scholar 

• Camastra F, Vinciarelli A (2003) Combining neural gas and learning vector quantization for cursive character recognition. Neuro-computing 51: 147–159

  Google Scholar 

• Chanda S, Pal U (2005) English, Devanagari and Urdu text identification. In: Proceedings of international conference on document analysis and recognition, pp 538–545

• Charles R, Giardina, Edward R, Dougherty (1988) Morphological methods in image and signal processing, Prentice Hall, Inc., p 217

• Chen J-L, Lee H-J (2001) Field data extraction for form document processing using a gravitation-based algorithm. Pattern Recognit 34: 1741–1750

  Article  MATH  Google Scholar 

• Cheng CK, Blumenstein M (2005) The neural based segmentation of cursive words using enhanced heuristics. In: Proceedings of the eighth international conference on document analysis and recognition, vol 2, pp 650–654

• Choudhuri S, Harit G, Madnani S, Shet RB (2000) Identification of scripts of Indian languages by combining trainable classifiers. In: Proceedings of international conference on vision, graphics and image processing

• Cote M, Lecolinet E, Cheriet M, Suen CY (1998) Automatic reading of cursive scripts using a reading model and perceptual concepts—the PERCEPTO system. Int J Doc Anal Recognit 1(1): 3–17

  Article  Google Scholar 

• Dimauro G, Impedovo S, Pirlo G, Salzo A (1997) In: Removing underlines from handwritten text: an experimental investigation. Downton AC, Impedovo S (eds) Progress in Handwriting Recognition. World Scientific Publishing, pp 497–501

• Dong J-X, Dominique P, Krzyyzak A, Suen C-Y (2005) Cursive word skew/slant corrections based on radon transform. In: Proceedings of the eighth international conference on document analysis and recognition, pp 478–483

• Elarbi-Boudihir M, Rehman A, Saba T (2011) Video motion perception using operation Gabor filter. Int J Phys Sci 6(12): 2799–2806

  Google Scholar 

• El-Yacoubi A, Gilloux M, Sabourin R, Suen CY (1999) An HMM-based approach for on-line unconstrained handwritten word modeling and recognition. IEEE Trans Pattern Anal Mach Intell 21(8): 752–760

  Article  Google Scholar 

• Fan KC, Wang LS, Tu YT (1998) Classification of machine-printed and handwritten texts using character block layout variance. Pattern Recognit 31(9): 1275–1284

  Article  Google Scholar 

• Foley JD, Dam AV, Feiner SK, Hughes JF (1997) Computer graphics: principles and practice in C, 2nd edn. Addison-Wesley, Pearson Education, Reading, MA

• Gader PD, Mohamed M, Chiang JH (1997) Handwritten word recognition with character and inter-character neural networks. IEEE Trans Syst Man Cybern Part B Cybern 27: 158–164

  Article  Google Scholar 

• Govindaraju V, Srihari SH (1992) In: Separating handwritten text from interfering strokes. Impedovo S, Simon JC (eds) From pixels to features III—frontiers in handwriting recognition. North-Holland Publication, Amsterdam, pp 17–28

• Guo JK, Ma MY (2001) Separating from machine printed text using hidden Markov model. In: Proceedings of the international conference on document analysis and recognition, pp 439–443

• Haron H, Rahim S, Rehman A, Saba T (2010) Curve length estimation using vertix chain code. Int J Comput Sci Eng 2(6): 2110–2113

  Google Scholar 

• Haron H, Rehman A, Adi DIS, Lim SP, Saba T (2012) Parameterization method on B-spline curve. Math Probl Eng 2012. doi:10.1155/2012/640472

• Haron H, Rehman A, Wulandhari LA, Saba T (2011) Improved vertex chain code algorithm for curve length estimation. J Comput Sci 7(5): 736–743. doi:10.3844/jcssp.2011.736.743

  Article  Google Scholar 

• Harouni M, Rahim MSM, Mohamad D, Rehman A, Saba T (2012) Online cursive Persian/Arabic character recognition by detecting critical points. Int J Acad Res 4(2): 208–213

  Google Scholar 

• Hochberg J, Kelly P, Thomas T, Kerns L (1997) Automatic script identification from document images using cluster-based templates. IEEE Trans Pattern Anal Mach Intell 19(2): 176–181

  Article  Google Scholar 

• Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8: 179–187

  MATH  Google Scholar 

• Imade S, Tatsuta S, Wada T (1993) Segmentation and classification for mixed text/image document using neural network. In: Proceedings of 2nd international conference on document analysis and recognition, pp 930–934

• Impedovo S, Ottaviano L, Occhinegro S (1991) Optical character recognition—a survey. Int J Pattern Recognit Artif Intell 5: 1–24

  Article  Google Scholar 

• Kavallieratou E, Stamatatos S (2004) Discrimination of machine-printed from handwritten text using simple structural characteristics. In: Proceedings of the 17th international conference on pattern recognition (ICPR’04), pp 437–440

• Kavallieratou E, Sgarbas K, Fakotakis N, Kokkinakis G (2003) Handwritten word recognition based on structural characteristics and lexical support. In: Proceedings of seventh international conference on document analysis and recognition, vol 1, pp 562–566

• Kim D (2003) Slant correction of handwritten strings based on structural properties of Korean characters. Pattern Recognit Lett 12: 2093–2101

  Google Scholar 

• Kim JH, Kim KK, Suen CY (2000) Hybrid schemes of homogeneous and heterogeneous classifiers for cursive word recognition. In: Proceedings of 7th international workshop on frontiers in handwriting recognition, pp 433–442

• Kimura F, Shridhar M (1991) Handwritten numerical recognition based on multiple algorithms. Pattern Recognit 24: 969–983

  Article  Google Scholar 

• Kimura F, Kayahara N, Miyake Y, Shridhar M (1997) Machine and human recognition of segmented characters from handwritten words. In: Proceedings of 4th international conference on document analysis and recognition (ICDAR ’97), pp 866–869

• Koerich AL, Ling LL (1998) A system for automatic extraction of the user-entered data from bank checks. In: Proceedings of international symposium on computer graphics, image processing and vision, 270–278

• Koyama J, Kato M, Hirose A (2008a) Distinction between handwritten and machine printed characters with no need to locate character or text line position. In: Proceedings of international joint conference on neural networks (IJCNN’08), pp 4044–4051

• Koyama J, Kato M, Hirose A (2008b) Handwritten character distinction method inspired by human vision mechanism. In: LNCS Springer, pp 1031–1040

• Krzyyzak A, Dai W, Suen CY (1990) Unconstrained handwritten character recognition using modified back propagation model. In: Proceedings of international workshop frontiers in handwritten recognition, April 1990, pp 145–153

• Kuhnke K, Simoncini L, Kovacs ZM (1995) A system for machine-written and hand-written character distinction. In: International conference on document analysis and recognition, vol 2, pp 811-814

• Kurniawan F, Rahim MSM, Daman D, Rehman A, Mohamad D, Mariyam S (2011) Region-based touched character segmentation in handwritten words. Int J Innov Comput Inf Control 7(6): 3107–3120

  Google Scholar 

• Kurniawan F, Rehman A, Mohamad D (2009a) Contour vs non-contour based word segmentation from handwritten text lines. An experimental analysis. Int J Digit Content Technol Appl 3(2): 127–131

  Google Scholar 

• Kurniawan F, Rehman A, Mohamad D (2009b) From contours to characters segmentation of cursive handwritten words with neural assistance. In: Proceedings of IEEE international conference on instrumentation, communications, information technology and biomedical engineering (ICICI-BME), pp 12–18

• Kurniawan F, Rehman A, Mohamed D, Mariyam S (2010) Self organizing features map with improved segmentation to identify touching of adjacent characters in handwritten words. In: Proceedings of IEEE ninth international conference on hybrid intelligent systems, 2009. HIS ’09, China, pp 475–480

• Lee H, Verma B (2008a) A novel multiple experts and fusion based segmentation algorithm for cursive handwriting recognition. In: Proceedings of the international joint conference on neural networks (IJCNN’08), pp 2994–2999

• Lee H, Verma B (2008b) Over-segmentation and validation strategy for offline cursive handwriting recognition. In: Proceedings of the international conference on intelligent servers, sensor networks and information processing, pp 91–96

• Liu K, Suen CY, Nadal C (1996) Automatic extraction of items from cheques images for payment recognition. In: International conference on pattern recognition, pp 798–802

• Ma H, Doermann D (2003) Gabor filter based multiclass classifier for scanned document images. In: Proceedings of 7th international conference on document analysis and recognition, pp 968–972

• Marti U, Bunke H (2001) Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int J Pattern Recognit Artif Intell 15(1): 65–90

  Article  Google Scholar 

• Mitrpanont JL, Limkonglap U (2007) Using contour analysis to improve feature extraction in Thai handwritten character recognition systems. In: Seventh IEEE international conference on computer and information technology, CIT 2007, pp 668–673

• Mohamad D, Rehman A, Kurniawan F (2008) A new approach for segmenting difficult cursive handwritten words from benchmark database. In: Proceedings of 4th international conference on information and communication technology and systems (ICTS) vol 1, pp 17–21

• Mohamad I, Rahim MSM, Bade A, Rehman A, Saba T (2012) Enhancement of the refinement process for surface of 3D object. J Am Sci 8(4): 358–365

  Google Scholar 

• Morita M, Facon J, Bortolozzi F, Garnes S, Sabourin R (1999) Mathematical morphology and weighted least squares to correct handwriting baseline skew. In: Proceedings of the international conference on document analysis and recognition, vol 1, pp 430–433

• Nikolaidis A, Strouthopoulos C (2008) Robust text extraction in mixed-type binary documents. In: Proceedings of the IEEE tenth workshop on multimedia signal processing, pp 393–398

• Nitz K, Cruz W, Aradhye H, Shaham T, Myers G (2003) An image-based mail facing and orientation system for enhanced postal automation. In: Proceedings of 7th international conference on document analysis and recognition, pp 694–698

• Norouzi A, Saba T, Rahim MSM, Rehman A (2012) Visualization and segmentation of 3D bone from CT images. Int J Acad Res 4(2): 201–207

  Google Scholar 

• Oh S, Suen CY (1998) Distance features for neural network-based recognition of handwritten characters. Int J Doc Anal Recognit 1(1): 73–88

  Article  Google Scholar 

• Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 9(1): 63–66

  MathSciNet  Google Scholar 

• Pal U, Sinha S, Chaudhuri BB (2003) Multi-script line identification from Indian documents. In: Proceedings of the seventh international conference on document analysis and recognition, vol 2, pp 880–884

• Phetchanchai C, Selamat A, Rehman A, Saba T (2010) Index financial time series based on Zigzag-perceptually important points. J Comput Sci 6(12): 1389–1395

  Article  Google Scholar 

• Rahim MSM, Rehman A, Faizal-Ab-Jabal M, Saba T (2011) Close spanning tree approach for error detection and correction for 2D CAD drawing. Int J Acad Res 3(4): 525–535

  Google Scholar 

• Rahim MSM, Rehman A, Kumoi R, Abdullah N, Saba T (2012a) FiLeDI framework for measuring fish length from digital images. Int J Phys Sci 7(4): 607–618. doi:10.5897/IJPS11.1581

  Google Scholar 

• Rahim MSM, Rehman A, Sholihah N, Kurniawan F, Saba T, Mohamad D (2012b) Region-based features extraction in ear biometrics. Int J Acad Res 4(1): 37–42

  Google Scholar 

• Raju SS, Pati PB, Ramakrishnan AG (2004) Gabor filter based block energy analysis for text extraction from digital document images. In: Proceedings of the first international workshop on document image analysis, pp 233–243

• Rehman A (2010) Offline cursive character recognition based on heuristics techniques. PhD thesis, Universiti Teknologi Malaysia, pp 80–85

• Rehman A, Mohamad D (2008) A simple segmentation approach for unconstrained cursive handwritten words in conjunction of neural network. Int J Image Process 2(3): 29–35

  Google Scholar 

• Rehman A, Saba T (2011a) Document skew estimation and correction: analysis of techniques, common problems and possible solutions. Appl Artif Intell 25(9): 769–787

  Article  Google Scholar 

• Rehman A, Saba T (2011b) Performance analysis of segmentation approach for cursive handwritten word recognition on benchmark database. Digit Signal Process 21: 486–490

  Article  Google Scholar 

• Rehman A, Saba T (2012a) Features extraction for soccer video semantic analysis: current achievements and remaining issues. Artif Intell Rev. doi:10.1007/s10462-012-9319-1

• Rehman A, Saba T (2012b) Analysis of advanced image processing to clinical and preclinical decision making with prospectus of quantitative imaging biomarkers. Artif Intell Rev. doi:10.1007/s10462-012-9335-1

• Rehman A, Saba T (2013) An improved intelligent model for visual scene analysis and compression. Int Arab J Inf Technol IAJIT (ISI indexed) (accepted)

• Rehman A, Kurniawan F, Mohamad D (2008a) Off-line cursive handwriting segmentation, a heuristic rule-based approach. J Inst Math Comput Sci (Computer Science Series) 19(2): 135–139

  Google Scholar 

• Rehman A, Kurniawan F, Mohamed D (2008b) Off-line cursive character recognition based on hybrid statistical features. In: International graduate conference on engineering and science, UTM Skudai (IGCES, 08)

• Rehman A, Mohamad D, Kurniawan F (2008c) Line and skew removal from off-line cursive handwritten words. Int J Res Sci 24(2): 28–33

  Google Scholar 

• Rehman A, Mohamad D, Sulong G, Saba T (2009a) Simple and effective techniques for core zone detection and slant correction in script recognition. In: The IEEE international conference on signal and image processing applications (ICSIPA’09), pp 15–20

• Rehman A, Mohamad D, Sulong G (2009b) Implicit vs explicit based script segmentation and recognition: a performance comparison on benchmark database. Int J Open Probl Comput Sci Math 2(3): 352–364

  Google Scholar 

• Rehman A, Kurniawan F, Mohamad D (2009c) Neuro-heuristic approach for segmenting cursive handwritten words. Int J Inf Process IJIP 3(2): 37–46 ISSN 0973-8215

  Google Scholar 

• Rehman A, Mohamad D, Kurniawan F (2009d) An automated approach to remove line from text bypassing restoration stage. In: Proceedings of 2nd IEEE international conference on computer, control and communication, pp 1–4

• Rehman A, Saba T, Sulong G (2010) An intelligent approach to image denoising. J Theor Appl Inf Technol 17(1): 32–36

  Google Scholar 

• Rehman A, Kurniawan F, Saba T (2011) An automatic approach for line detection and removal without characters smash-up. Imaging Sci J 59: 171–182

  Article  Google Scholar 

• Saba T (2012a) Offline cursive touched script non-linear segmentation. PhD thesis, Universiti Teknologi Malaysia, pp 102–115

• Saba T (2012b) Offline cursive touched script non-linear segmentation, PhD thesis, Universiti Teknologi Malaysia, pp 133–138

• Saba T, Rehman A (2011) Off-line cursive script recognition: current advances, comparisons and remaining problems. Artif Intell Rev 37(4): 261–268. doi:10.1007/s10462-011-9229-7

  Google Scholar 

• Saba T, Rehman A (2012) Effects of artificially intelligent tools on pattern recognition. Int J Mach Learn Cybern. doi:10.1007/s13042-012-0082-z

• Saba T, Rehman A, Sulong G (2009) ITS: using A.I. to improve character recognition of students with intellectual disabilities. In: International conference on software engineering and computer systems, UMP Malaysia, pp 5–9

• Saba T, Rehman A, Sulong G (2010a) Improved offline connected script recognition based on hybrid strategy. Int J Eng Sci Technol 2(6): 1603–1611

  Google Scholar 

• Saba T, Rehman A, Sulong G (2010b) Non-linear segmentation of touched Roman characters based on genetic algorithm. Int J Comput Sci Eng 2(6): 2167–2172

  Google Scholar 

• Saba T, Rehman A, Sulong G (2011a) Cursive script segmentation with neural confidence. Int J Innov Comput Inf Control IJICIC 7(7): 1–10

  Google Scholar 

• Saba T, Rehman A, Sulong G (2011b) Improved statistical features for cursive character recognition. Int J Innov Comput Inf Control IJICIC 7(9): 5211–5224

  Google Scholar 

• Saba T, Sulong G, Rehman A (2011c) Document image analysis: issues, comparison of methods and remaining problems. Artif Intell Rev 35(2): 101–118. doi:10.1007/s10462-010-9186-6

  Article  Google Scholar 

• Saba T, Rehman A, Elarbi-Boudihir M (2011d) Methods and strategies on off-line cursive touched characters segmentation: a directional review. Artif Intell Rev. doi:10.1007/s10462-011-9271-5

• Serra J (1982) Image analysis and mathematical morphology. Academic Press, London

  MATH  Google Scholar 

• Shridhar M, Badreldin A (1984) High accuracy character recognition using Fourier and topological descriptors. Pattern Recognit 17: 515–524

  Article  MATH  Google Scholar 

• Shridhar M, Kimura F (1995) Handwritten address interpretation using word recognition with and without lexicon. In: Proceedings of the IEEE international conference on systems, man and cybernetics, Piscataway, NJ, USA, vol 3, 2341–2346

• Singh S, Hewitt M (2000) Cursive digit and character recognition on CEDAR database. In: Proceedings of international conference on pattern recognition, pp 569–572

• Spitz AL (1997) Determination of the script and language content of document images. IEEE Trans Pattern Anal Mach Intell 19(3): 235–245

  Article  Google Scholar 

• Srihari SN, Shim YC, Ramanprasad V (1994) A system to read names and address on tax forms, Technical Report CEDARTR-94-2, CEDAR, SUNY, Buffalo, NY

• Suen CY (1986) Character recognition by computer and applications. In: Young TY, Fu K-S (eds) Handbook of pattern recognition and image processing. Academic Press Inc., San Diego, CA, pp 569–586

  Google Scholar 

• Sulong G, Saba T, Rehman A (2010) Dynamic programming based hybrid strategy for offline cursive script recognition. In: IEEE second international conference on computer and engineering, vol 2, pp 580–584

• Sulong G, Saba T, Rehman A, Saparudin (2009) A new scars removal technique of fingerprint images. In: IEEE international conference on instrumentation communication, information technology and biomedical engineering (ICICI-BME), Bandung, Indonesia, pp 31–35

• Trier OD, Jain AK (1995) Evaluation of binarization methods for document images. IEEE Trans Pattern Anal Mach Intell 17: 312–315

  Article  Google Scholar 

• Uchida S, Taira E, Sakoe H (2001) Non-uniform slant correction using dynamic programming. In: Proceedings of 6th international conference on document analysis and recognition, vol 1, pp 434–438

• Vamvakas G, Gatos B, Pratikakis I, Stamatopoulos N, Roniotis A, Perantonis SJ (2007) Hybrid off-line OCR for isolated handwritten greek characters. In: Proceedings of fourth IASTED international conference on signal processing, pattern recognition and applications, pp 197–202

• Verma B (2002) A contour character extraction approach in conjunction with a neural confidence fusion technique for the segmentation of handwriting recognition. In: Proceeding of the 9th international conference on neural information processing, vol 5, pp 2459–2463

• Verma B, Blumenstein M, Ghosh M (2004) A novel approach for structural feature extraction: contour vs. direction. Pattern Recognit Lett 25(9): 975–988

  Article  Google Scholar 

• Vijaya PA, Padma MC (2009) Text line identification from a multilingual document. In: Proceedings of the international conference on digital image processing, pp 302–305

• Vinciarelli A, Luettin J (2001) A new normalization technique for cursive handwritten words. Pattern Recognit Lett 22: 1043–1050

  Article  MATH  Google Scholar 

• Wang D, Srihari SN (1991) Analysis of form images. In: Proceedings of first international conference on document analysis and recognition, Saint Malo, France, pp 181–191

• Xingyuan L, Wen G (1999) A robust method for unknown structure form analysis. J Softw 10(11): 1216–1224

  Google Scholar 

• Xiuling H, Yang Y, Zengzhao C, Ying Y, Cailin D (2006) Field extraction based on two-level regulated HMT in auto form processing. In: Proceedings of sixth international conference on intelligent systems design and applications, (ISDA’06), vol 2, pp 716–719

• Yamada H, Nakano Y (1996) Cursive handwritten word recognition using multiple segmentation determined by contour analysis. IEICE Trans Inf Syst E79-D:464–470

• Yong JY, Kim MK, Bana SW, Kwon YB (1997) Line removal and restoration of handwritten characters on the form documents. In: Proceedings of the fourth international conference on document analysis and recognition, vol 1, pp 128–131

• Yoo J-Y, Kim M-K, Han SY, Kwon Y-B (1997) Line removal and restoration of handwritten characters on the form documents. In: Proceedings of the fourth international conference on document analysis and recognition, vol 1, pp 128–131

• Yu B, Jain AK (1996) A generic system for form dropout. IEEE Trans Pattern Anal Mach Intell 18(11): 1127–1131

  Article  Google Scholar 

• Yuan J, Tang Y, Suen CY (1995) Four directional adjacency graphs (FDAG) and their application in locating fields in forms. In: Proceeding of 3rd international conference on document analysis and recognition, Montreal, Canada

• Zheng Y, Li H, Doermann D (2004) Machine printed text and handwriting identification in noisy document images. IEEE Trans Pattern Anal Mach Intell 26(3): 337–353

  Article  Google Scholar 

• Zheng Y, Liu C, Ding X (2002) Single character type identification. In: Proceedings of SPIE conference on document recognition and retrieval, pp 49–56

• Zhou L, Lu Y, Tan CL (2006) Bangla/English script identification based on analysis of connected component profiles. In: Proceedings of 7th document analysis systems, pp 243–254

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