Skip to main content
SpringerLink
Log in

Neural networks for document image preprocessing: state of the art

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Neural network are most popular in the research community due to its generalization abilities. Additionally, it has been successfully implemented in biometrics, features selection, object tracking, document image preprocessing and classification. This paper specifically, clusters, summarize, interpret and evaluate neural networks in document Image preprocessing. The importance of the learning algorithms in neural networks training and testing for preprocessing is also highlighted. Finally, a critical analysis on the reviewed approaches and the future research guidelines in the field are suggested.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information Sciences, Al-Imam M. Saud Islamic University, Riyadh, Kingdom of Saudi Arabia

    Amjad Rehman

  2. Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Tanzila Saba

Authors
  1. Amjad Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tanzila Saba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amjad Rehman.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rehman, A., Saba, T. Neural networks for document image preprocessing: state of the art. Artif Intell Rev 42, 253–273 (2014). https://doi.org/10.1007/s10462-012-9337-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-012-9337-z

Keywords

Search

Navigation