Off-line cursive script recognition: current advances, comparisons and remaining problems

• Aburas AA, Rehiel SA (2008) New promising off-line tool for Arabic handwritten character recognition based On JPEG2000 image compression. In: Proceedings of the 3rd international conference on introduction and communication technology. From theory to applications (ICTTA, 08), pp 1–5

• Alkoot M, Kittler J (1999) Experimental evaluation of expert fusion strategies. Pattern Recognit Lett 20(11–13): 1361–1369

  Google Scholar 

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

  Google Scholar 

• Arica N, Yarman-Vural FT (2002) Optical character recognition for cursive handwriting. IEEE Trans Pattern Anal Mach Intell 24(6): 801–813

  Google Scholar 

• Blumenstein M, Verma B (1997) A segmentation algorithm used in conjunction with artificial neural networks for the recognition of real-world postal addresses. In: Proceedings of 2nd online world conference on soft computing in engineering design and manufacturing

• Blumenstein M, Verma B (1998a) An artificial neural network based segmentation algorithm for off-line handwriting recognition. In: Proceedings of the 2nd international conference on computational intelligence and multimedia applications, Gippsland, pp 306–311

• Blumenstein M, Verma B (1998b) A neural based segmentation and recognition technique for handwritten words. In: Proceedings of the world congress on computational intelligence, Anchorage, pp 1738–1742

• Blumenstein M, Verma B (1998c) Conventional vs. neuro-conventional segmentation techniques for handwriting recognition: a comparison. In: Proceedings of the 2nd IEEE international conference on intelligent processing systems, Gold Coast, pp 473–477

• Blumenstein M., Verma B (1999a) Neural solutions for the segmentation and recognition of difficult words from a benchmark database. In: Proceedings of the 5th international conference on document analysis and recognition, Bangalore, pp 281–284

• Blumenstein M, Verma B (1999b) A new segmentation algorithm for handwritten word recognition. In: Proceedings of the international joint conference on neural networks, Washington, vol 4, pp 878–882

• Blumenstein M, Verma B (2001) Analysis of segmentation performance on the CEDAR benchmark database. In: Proceedings of 6th international conference on document analysis and recognition, pp 1142–1146

• Blumenstein M, Verma B, Basli H (2003) A novel feature extraction technique for the recognition of segmented handwritten characters. In: Fairhurst M, Downton A (eds) Proceedings of the 7th international conference on document analysis and recognition, pp 137–141

• 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

  MATH  Google Scholar 

• Bortolozzi F, de Souza Britto A, Jr, Oliveira LS, Morita M (2005) Recent advances in handwriting recognition. In: Pal U, Parui SK, Chaudhuri BB (eds) Document analysis, pp 1–30

• Bose CB, Kuo S (1994) Connected and degraded text recognition using hidden Markov model. Pattern Recognit 27(10): 1345–1363

  Google Scholar 

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

  Google Scholar 

• Breiman L (1996) Bagging predictors. Mach Learn 24(2): 123–140

  MathSciNet  MATH  Google Scholar 

• Bretto A, Azema J, Cherifi H, Laget B (1997) Combinatorics and image processing. Graph Models Image Proces 59: 256–277

  Google Scholar 

• Bretto A, Cherifi H, Aboutajdine D (2002) Hypergraph imaging: an Overview. Pattern Recognit 35: 651–658

  MATH  Google Scholar 

• Breukelen TM, Duin R, Kittler J (2000) Combining multiple classifiers by averaging or by multiplying?. Pattern Recognit 33(9): 1475–1485

  Google Scholar 

• Britto A Jr, Sabourin R, Bortolozzi F, Suen CY (2001a) An enhanced HMM topology in an LBA framework for the recognition of handwritten numeral strings. Proc Int Conf Adv Pattern Recognit 1: 105–114

  Google Scholar 

• Britto A Jr, Sabourin R, Bortolozzi F, Suen CY (2001b) A two-stage HMM-based system for recognizing handwritten numeral strings. In: Proceedings of the international conference on document analysis and recognition, Seattle, pp 396–400

• Britto A Jr, Sabourin R, Bortolozzi F, Suen C-Y (2002) A string length predictor to control the level building of HMMs for handwritten numeral recognition. In: Proceedings of 16th international conference on pattern recognition, vol 4, pp 31–34

• Britto A Jr, Sabourin R, Bortolozzi F, Suen CY (2004) Foreground and background information in an HMM-based method for recognition of isolated characters and numeral strings. In: Proceedings of the 9th international workshop on frontiers in handwriting recognition, pp 371–376

• Bruel T (1994) Design and implementation of a system for recognition of handwritten responses on US census forms. In: Proceedings of the IAPR workshop on document analysis systems, Kaiserlautern, pp 237–264

• Bunke H, Roth M, Schukat-Talamazzini E-G (1995) Off-line cursive handwriting recognition using hidden Markov models. Pattern Recognit 28(9): 1399–1413

  Google Scholar 

• Burges CJC, Be JI, Nohl CR (1992) Recognition of handwritten cursive postal words using neural networks. In: Proceedings of the 5th United States Postal Service (USPS) advanced technology conference, pp 117–124

• Burges CJC, Denker JS, Lecun Y, Nohi CR (1993) Off-line recognition of handwritten postal words using neural networks. Int J Pattern Recognit Artif Intell 7(4): 689–704

  Google Scholar 

• Caesar T, Gloger JM, Mandler E (1993) Preprocessing and feature extraction for a handwriting recognition system. In: Proceedings of international conference on document analysis and recognition, pp 408–411

• Cai J, Liu Z-Q (1999) Integration of structural and statistical information for unconstrained handwritten numeral recognition. IEEE Trans Pattern Anal Mach Intell 21(3): 263–270

  MathSciNet  Google Scholar 

• Camastra F (2007) A SVM-based cursive character rcognizer. Pattern Recognit 40(12): 3721–3727

  MATH  Google Scholar 

• Camastra F, Vinciarelli A (2001) Cursive character recognition by learning vector quantization. Pattern Recognit Lett 22: 625–629

  MATH  Google Scholar 

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

  Google Scholar 

• Cao J, Ahmadi M, Shridhar M (1995) Recognition of handwritten numerals with multiple feature and multistage classifier. Pattern Recognit 28(3): 153–159

  Google Scholar 

• Casey RG (1992) Segmentation of touching characters in postal addresses. In: Proceedings of the 5th USPS advanced technology conference, pp 743–754

• Casey RG, Lecolinet E (1996) A survey of methods and strategies in character segmentation. IEEE Trans Pattern Anal Mach Intell 18: 690–706

  Google Scholar 

• Cavalin PR, Britto AS, Bortolozzi F, Sabourin R, Oliveira LS (2006) An implicit segmentation based method for recognition of handwritten strings of characters. In: Proceedings of ACM symposium on applied computing, pp 836–840

• Chellapilla K, Shilman M, Simard P (2006) Combining multiple classifiers for faster optical character recognition. In: Proceedings of international conference on document analysis systems, LNCS 3872. Springer, pp 358–367

• Chen M-Y, Kundu A (1993) An alternative approach to variable duration HMM in handwritten word recognition. In: Proceedings of the 3rd international workshop on frontiers in handwriting recognition, Buffalo, pp 82–91

• Chen Y, Leedham G (2005) Independent component analysis segmentation algorithm. In: Proceedings of the 8th international conference on document analysis and recognition (ICDAR’05), vol 2, pp 680–684

• Chen M-Y, Kundu A, Zhou J, Srihari SN (1992) Off-line handwritten word recognition using hidden Markov model. In: Proceedings of the 5th USPS advanced T

• Chen M-Y, Kundu A, Zhou J (1994) Off-line handwritten word recognition using a HMM type stochastic network. IEEE Trans Pattern Anal Mach Intell 16(5): 481–496

  Google Scholar 

• Chen M-Y, Kundu A, Srihari SN (1995) Variable duration hidden Morkov model and morphological segmentation for handwritten word recognition. IEEE Trans Image Process 4(12): 1675–1687

  Google Scholar 

• Cheng CK, Blumenstein M (2005a) The neural based segmentation of cursive words using enhanced heuristics. In: Proceedings of the 8th international conference on document analysis and recognition, vol 2, pp 650–654

• Cheng CK, Blumenstein M (2005b) Improving the segmentation of cursive handwritten words using ligature detection and neural validation. In: Proceedings of the 4th Asia Pacific international symposium on information technology (APIS 2005), Gold Coast, pp 56–59

• Cheng H, Hsu WH, Kuo MC (1993) Recognition of hand printed Chinese characters via stroke relaxation. Pattern Recognit 26(4): 579–593

  Google Scholar 

• Cheng CK, Liu XY, Blumenstein M, Muthukkumarasamy V (2004) Enhancing neural confidence-based segmentation for cursive handwriting recognition. In: 5th International conference on simulated evolution and learning, Busan, SWA-8

• Cheriet M (1993) Reading cursive script by parts. In: Proceedings of the 3rd international workshop on frontiers in handwriting recognition, Buffalo, 25–27 May, pp 403–408

• Cheriet M, Kharma N, Liu C-Lin, Suen C-Y (2007) Character recognition systems (OCR). Wiley, pp 204–206

• Chiang J-H (1998) A hybrid neural model in handwritten word recognition. Neural Netw 11(2): 337–346

  Google Scholar 

• Cho W, Lee SW, Kim JH (1995) Modeling and recognition of cursive words with hidden Markov models. Pattern Recognit 28(12): 1941–1953

  Google Scholar 

• Dawoud A (2007) Iterative cross section sequence graph for handwritten character segmentation. IEEE Trans Image Process 16(8): 2150–2154

  MathSciNet  Google Scholar 

• Dimauro D, Impedovo S, Pirlo G, Salzo A (1998) An advanced segmentation technique for cursive word recognition. In: Lee SW (ed) Advances in handwriting recognition. World Scientific, pp 255–264. Technology Conference, pp 563–579

• Dunn CE, Wang, PSP (1992) Character segmenting techniques for handwritten text—a survey. In: Proceedings of 11th international conference on pattern recognition, vol 2, pp 577–591

• Dzuba G, Filatov A, Gershuny D, Kill I (1998) Handwritten word recognition, the approach proved by practice. In: Proceedings of 6th international workshop on frontiers in handwriting recognition, Taejon, pp 99–111

• Eastwood B, Jennings A, Harvey A (1997) Neural network based segmentation handwritten words. In: Proceedings of 6th international conference on image processing and its applications, vol 2, pp 750–755

• Ehrich RW, Koehler KJ (1975) Experiments in the contextual recognition of cursive script. IEEE Trans Comput 24: 182–194

  MATH  Google Scholar 

• Elliman DG, Lancaster IT (1990) A review of segmentation and contextuel analysis techniques for text recognition. Pattern Recognit 23(3–4): 337–346

  Google Scholar 

• Elms AJ, Procter S, Illingworth J (1989) The advantage of using an HMM-based approach for faxed word recognition. Int J Document Anal Recognit 1: 18–36

  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

  Google Scholar 

• Farah N, Souici L, Sellami M (2005) Arabic word recognition by classifiers and context. J Comput Sci Technol 20(3): 402–410

  Google Scholar 

• Farouz C, Gilloux M, Bertille JM (1998) Handwritten word recognition with contextual hidden Morkov models. In: Proceedings of 6th international workshop on frontiers in handwriting recognition, Taejon, pp 133–142

• Favata JT (1997) Character model word recognition. In: Downton AC, Impedovo S (eds) Progress in handwriting recognition, pp 57–61

• Favata JT (2001) Off-line general handwritten word recognition using an approximate beam matching algorithm. IEEE Trans Pattern Anal Mach Intell 23: 393–398

  Google Scholar 

• Favata JT, Srihari SN (1992) Recognition of general handwritten words using hypothesis generation and reduction methodology. iN: Proceedings of the 5th USPS advanced technology conference, pp 237–251

• Freitas F, Bortolozzi, Sabourin R (2001, September) Handwritten isolated word recognition: an approach based on mutual information for feature set validation. In: Proceedings of 6th international conference on document analysis and recognition, Seattle, pp 665–669

• Frishkopf LS, Harmon LD (1961) Machine reading of cursive script. In: Cherry C (eds) Information theory. Butterworth, London, pp 300–316

  Google Scholar 

• Fujisawa H, Nakano Y, Kurino K (1992) Segmentation methods for character recognition: from segmentation to document structure analysis. Proc IEEE 80(7): 1079–1092

  Google Scholar 

• Fukushima K, Imagawa T (1993) Recognition and segmentation of connected characters with selective attention. Neural Netw 6: 33–41

  Google Scholar 

• Gader PD, Khabou MA (1996) Automatic feature generation for handwritten digit recognition. IEEE Trans Pattern Anal Mach Intell 18(12): 1256–1261

  Google Scholar 

• Gader PD, Mohammed MA, Chiang JH (1994) Handwritten word recognition with character and inter character neural networks. IEEE Trans Syst Man Cybern B 27: 158–164

  Google Scholar 

• Gader PD, Whalen M, Ganzberger M, Hepp D (1995) Hand-printed word recognition on a NIST data set. Mach Vis Appl 8: 31–41

  Google Scholar 

• Gader PD, Mohamed M, Chiang J-H (1997) Handwritten word recognition with character and inter-character neural networks. IEEE Trans Syst Man Cybern B Cybern 27(1): 158–164

  Google Scholar 

• Gang L, Verma B, Kulkarni S (2002) Experimental analysis of neural network based feature extractors for cursive handwriting recognition. In: Proceedings of the IEEE world congress on computational intelligence, pp 2837–2841

• Gatos B, Pratikakis I, Perantonis SJ (2006a) Hybrid off-line cursive handwriting word recognition. In: Proceedings of 18th international conference on pattern recognition (ICPR’06), vol 2, pp 998–1002

• Gatos B, Pratikakis I, Kesidis AL, Perantonis SJ (2006b) Efficient off-line cursive handwriting word recognition. In: Proceedings of the 10th international workshop on frontiers in handwriting recognition

• Gatos B, Antonacopoulos A, Stamatopoulos N (2007) ICDAR 2007 handwriting segmentation context. In: Proceedings of the international conference on document analysis and recognition, pp 1284–1288

• Ghosh M, Ghosh R, Verma B (2004) A fully automated off-line handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier. Int J Pattern Recognit Artif Intell 18(7): 1267–1283

  Google Scholar 

• Gillies M (1992) Cursive word recognition using hidden markov models. In: Proc fifth US postal service advanced technology conference, pp 557–562

• Gilloux M (1993) Hidden Markov models in handwriting recognition, fundamentals in handwriting recognition. In: Impedovo S (eds) NATO ASI Series F: Computers and Systems Sciences, vol 124. Springer, New York, pp 264–288

  Google Scholar 

• Gilloux M, Bertille JM, Leroux M (1993) Recognition of handwritten words in a limited dynamic vocabulary. In: Proceedings of the 3rd international workshop on frontiers in handwriting recognition, Buffalo, 25–27 May, pp 417–422

• Gilloux M, Leroux M, Bertille J-M (1995a) Strategies for cursive script recognition using hidden Morkov models. Mach Vis Appl 8: 197–205

  Google Scholar 

• Gilloux M, Lemarie B, Leroux M (1995b) A hybrid radial basis function/hidden Morkov model handwritten word recognition system. In: International conference on document analysis and recognition, Montreal, pp 394–397

• Govindan VK, Shivaprasad AP (1990) Character recognition—a review. Pattern Recognit 23: 671–683

  Google Scholar 

• Grandidier F (2003) Un Nouvel Algorithme de Sélection de Caractéristiques-Application à la Lecture Automatique de l’ecriture Manuscrite. PhD thesis, École de Technologie Supérieure, Montreal-Canada, Janvier

• Guillevic D, Suen CY (1993) Cursive script recognition: a fast reader scheme. In: Proceedings of the 3rd international conference on documents analysis and recognition, pp 311–314

• Guillevic D, Suen C (1998) HMM-KNN word recognition engine for bank check processing. In Proceedings of international conference on pattern recognition, Brisbane, pp 1526–1529

• Günter S, Bunke H (2003) Ensembles of classifiers for handwritten word recognition. Int J Document Anal Recognit 5: 224–232

  Google Scholar 

• Günter S, Bunke H (2004) Feature selection algorithms for the generation of multiple classier systems and their application to handwritten word recognition. Pattern Recognit Lett 25(11): 1323–1336

  Google Scholar 

• Günter S, Bunke H (2005) Off-line cursive handwriting recognition using multiple classifier systems. On the influence of vocabulary, ensemble, and training set size. Optics Lasers Eng 43(3–5): 437–454

  Google Scholar 

• Guyon (1996) Handwritten synthesis from handwritten glyphs. In: 5th International workshop on frontiers of handwriting recognition, pp 309–312

• Ha T, Bunke H (1997) Off-line handwritten numeral recognition by perturbation method. IEEE Trans Pattern Anal Mach Intell 19(5): 535–539

  Google Scholar 

• Ha T, Zimmermann M, Bunke H (1998) Off-line handwritten numeral string recognition by combining segmentation-based and segmentation-free methods. Pattern Recognit 31(3): 257–272

  Google Scholar 

• Hamamura T, Akagi T, Irie B (2007) An analytic word recognition algorithm using a posteriori probability. Proc Int Conf Document Anal Recognit 02: 669–673

  Google Scholar 

• Han K, Sethi IK (1995) Off-line cursive handwriting segmentation. In: Proceedings of the 3rd international conference on documents analysis and recognition, pp 894–897

• Hanhong V (2002) Incorporating contextual character geometry in word recognition. In: Proceedings of 8th international workshop on frontiers in handwriting recognition, pp 123–127

• Helmers M, Bunke H (2003) Generation and use of the synthetic training data in cursive handwriting recognition. In: First Iberian conference on pattern recognition and image analysis, pp 336–345

• Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8): 832–844

  Google Scholar 

• Holt M, Beglou M, Datta S (1992) Slant-independent letter segmentation for off-line cursive script recognition. In: Impedovo S, Simon JC (eds) From pixels to features III. Elsevier, Amsterdam, p 41

  Google Scholar 

• Howe NR, Rath TM, Manmatha R (2005) Boosted decision trees for word recognition in handwritten document retrieval. In: Proceedings of the 28th annual SIGIR conference on research and development in information retrieval, pp 377–383

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

  MATH  Google Scholar 

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

  Google Scholar 

• Kang KW, Kim JH (2004) Utilization of hierarchical, stochastic relationship modeling for Hangul character recognition. IEEE Trans Pattern Anal Mach Intell 26(9): 1185–1196

  MathSciNet  Google Scholar 

• Kavallieratou E, Stamatatos E, Fakotakis N, Kokkinakis G (2000b) Handwritten character segmentation using transformation-based learning. In: Proceedings of 15th international conference on pattern recognition, vol 2, pp 634–637

• Kim G, Govindaraju V (1997) A Lexicon driven approach to handwritten word recognition for real-time applications. IEEE Trans Pattern Anal Mach Intell 19(4): 366–379

  Google Scholar 

• Kim JH, Kim KK, Suen CY (2000) An HMM-MLP hybrid model for cursive script recognition. Pattern Anal Appl 3: 314–324

  MathSciNet  Google Scholar 

• Kim KK, Kim JH, Suen CY (2002) Recognition of handwritten touching pairs of digits using structural features. Pattern Recognit 23(1): 13–21

  MATH  Google Scholar 

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

  Google Scholar 

• Kimura F, Tsuruoka S, Shridhar M, Chen Z (1992) Context-directed handwritten word recognition for postal service applications. In: Proceedings of the 5th USPS advanced technology conference, pp 199–213, 145

• Kimura F, Shridhar M, Chen Z (1993) Improvements of a Lexicon directed algorithm for recognition of unconstrained handwritten words. In: Proceedings of the 2nd international conference on document analysis and recognition, Tsukuba, pp 18–22

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

• Knerr S, Anisimov V, Baret O, Gorski N, Price D, Simon JC (1997) The A^@IA inter-check system. Courtesy amount and legal amount recognition for French Checks”. Automatic bank cheque processing 43–86

• Knerr S, Augustin E, Baret O, Price D (1998) Hidden Markov model based word recognition and its application to legal amount reading on French checks. Comput Vis Image Understand 70(3): 404–419

  Google Scholar 

• Knoll AL (1969) Experiments with characteristic loci for recognition of handprinted characters. IEEE Trans Comput 18:366–372

  Google Scholar 

• Koch, Paquet T, Heutte L (2004) Combination of contextual information for handwritten word recognition. In: 9th International workshop on frontiers in handwriting recognition, Kokubunji, pp 468–473

• Koerich AL, Sabourin R, Suen CY (2003) Large vocabulary off-line handwriting recognition: a survey. Pattern Anal Appl 6(2): 97–121

  MathSciNet  Google Scholar 

• Koerich L, Sabourin R, Suen C-Y (2004) Fast Two–Level HMM Decoding Algorithm for Large Vocabulary Handwriting Recognition, 9th International Workshop on Frontiers in Handwriting Recognition, 26–29 Oct, Kokubunji, pp 232–238

• Koerich AL, Sabourin R, Suen CY (2005) Recognition and verification of unconstrained handwritten words. IEEE Trans Pattern Anal Mach Intell 27(10): 1509–1522

  Google Scholar 

• Koerich AL, Britto A, Oliveira LES, Sabourin R (2006) Fusing high- and low-level features for handwritten word recognition. In: Proceedings of the 10th international workshop on frontiers in handwriting recognition

• Kundu YH, Chen M (2002) Alternatives to variable duration HMM in handwriting recognition. IEEE Trans Pattern Anal Mach Intell 20(11): 1275–1280

  Google Scholar 

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

• Lallican PM, Viard-Gaudin C (1998) Off-line handwriting modeling as a trajectory tracking Problem. In: International workshop on frontiers in handwriting recognition, IWFHR’6, Taejon, pp 347–356

• Lazzerini B, Marcelloni F (2000) A linguistic fuzzy recognizer of offline handwritten characters. Pattern Recognit Lett 21: 319–327

  Google Scholar 

• Lecolinet E, Crettez J-P (1991) A grapheme-based segmentation technique for cursive script recognition. In: Proceedings of the 1st international conference on document analysis and recognition, St Malo, pp 740–748

• LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11): 2278–2324

  Google Scholar 

• 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 J, Gader P (2002) Neural networks with enhanced outlier rejection ability for off-line handwritten word recognition. Pattern Recognit 35: 2061–2071

  MATH  Google Scholar 

• Liu C-L, Fujisawa H (2005) Classification and learning for character recognition: comparison of methods and remaining problems. In: Proceedings of the international workshop on neural networks and learning in document analysis and recognition, pp 5–7

• Liu C-L, Narukawa K (2004) Normalization ensemble for handwritten character recognition. In: 9th International workshop on frontiers of handwriting recognition, pp 69–74

• Liu C-L, Nakashima K, Sako H, Fujisawa H (2002) Handwritten digit recognition using state-of-the-art techniques. In: Proceedings of 8th international workshop on frontiers of handwriting recognition, pp 320–325

• Lorette G (1999) Handwriting recognition or reading? What is the situation at the dawn of the 3rd millennium?.  Int J Document Anal Recognit 2: 2–12

  Google Scholar 

• Lu Y (1995) Machine printed character segmentation—an overview. Pattern Recognit 28(1): 67–80

  Google Scholar 

• Lu Y, Shridhar M (1996) Character segmentation in handwritten words—an overview. Pattern Recognit 29: 77–96

  Google Scholar 

• Lu W, Ren Y, Suen CY (1991) Hierarchical attributed graph representation and recognition of handwritten Chinese characters. Pattern Recognit 24(7): 617–632

  Google Scholar 

• Madhvanath S, Govindaraju V (2001) The role of holistic paradigms in handwritten word recognition. IEEE Trans Pattern Anal Mach Intell 23(2): 149–164

  Google Scholar 

• Madhvanath S, Kleinberg E, Govindaraju V (1999) Holistic verification of handwritten phrases. IEEE Trans Pattern Anal Mach Intell 21: 1344–1356

  Google Scholar 

• Maier M (1986) Separating characters in scripted documents. In: Proceedings of the 8th international conference on pattern recognition, Paris, pp 1056–1058

• 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

  Google Scholar 

• Milgram J, Cheriet M, Sabourin R (2004) Speeding up the decision making of support vector classifier. In: Proceedings of 9th international workshop on frontiers in handwriting recognition, pp 57–62

• Mitrpanont JL, Limkonglap U (2007) Using contour analysis to improve feature extraction in Thai handwritten character recognition systems. In: 7th IEEE international conference on computer and information technology, CIT 2007, pp 668–673

• Mohamed M, Gader P (1996) Handwritten word recognition using segmentation-free hidden markov modeling and segmentation-based dynamic programming techniques. IEEE Trans Pattern Anal Mach Intell 18(5): 548–554

  Google Scholar 

• Mohamed MA, Gader P (2000) Generalized hidden Morkov models—part ii: application to handwritten word recognition. IEEE Trans Fuzzy Syst 8: 82–94

  Google Scholar 

• Mori M, Suzuki A, Siho A, Ohtsuka S (2000) Generating new samples from handwritten numerals based on point correspondence. In: 7th International workshop on frontiers of handwriting recognition, pp 281–290

• Morita M, Oliveira LS, Sabourin R (2004) Unsupervised feature selection for ensemble of classifiers. In: Proceedings of international workshop on frontiers in handwriting recognition-9, pp 81–86

• Nicchiotti G, Scagliola C, Rimassa S (2000) A simple and effective cursive word segmentation method. In: Proceedings of the 7th international workshop on frontiers in handwriting recognition, September, Amsterdam, ISBN 90-76942-01-3. International Unipen Foundation, Nijmegen, pp 499–504

• Nishimura M, Kobayashi M, Maruyama Y, Nakano (1999) Off-line character recognition using HMM by multiple directional feature extraction and voting with bagging algorithm. In: Proceedings of 5th international conference on document analysis and recognition, pp 49–52

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

  Google Scholar 

• Olivier C, Paquet T, Avila M, Lecourtier Y (1995) Recognition of handwritten words using Stochastic Models. In: International conference on document analysis and recognition, pp 19–23

• Oliveira LS, Sabourin R, Bortolozzi F, Suen CY (2002) Automatic recognition of handwritten numerical strings: a recognition and verification strategy. IEEE Trans Pattern Anal Mach Intell 24(11): 1438–1454

  Google Scholar 

• Oliveira LS, Sabourin R, Bortolozzi F, Suen CY (2003a) Feature selection for ensembles: a hierarchical multi-objective genetic algorithm approach. In: 7th International conference on document analysis and recognition, vol 2, pp 676–680

• Oliveira LS, Sabourin R, Bortolozzi F, Suen CY (2003b) Impacts of verification on a numeral string recognition system. Pattern Recognit Lett 24(7): 1023–1031

  Google Scholar 

• Oliveira LS, Britto AS, Sabourin R (2005) A synthetic database to assess segmentation algorithms. In: Proceedings of 8th international conference on document analysis and recognition, vol 1, pp 207–211

• Optiz DW (1999) Feature selection for Ensembles. In: 16th International conference on artificial intelligence, pp 379–384

• Pal U, Belaid A, Choisy C (2003) Touching numeral segmentation using water reservoir concept. Pattern Recognit Lett 24: 261–272

  Google Scholar 

• Partridge D, Yates WB (1996) Engineering multiversion neural-net systems. Neural Comput 8(4): 869–893

  Google Scholar 

• Pinales Ruiz J, Jaime-Rivas R, Castro MJ (2007) Discriminative capacity of perceptual features in handwriting recognition. Telecommun Radio Eng 64(11): 931–937

  Google Scholar 

• Plamondon R, Srihari SN (2000) On-line and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22: 63–84

  Google Scholar 

• Procter S, Elms AJ (1998) The recognition of handwritten digit strings of unknown length using hidden Markov models. In: Proceedings of the 14th international conference on pattern recognition (ICPR’98), pp 1515–1517

• Procter S, Illingworth J (1999) Handwriting recognition using HMMs and a conservative level building algorithm. In: Proceedings of 7th international Conference on image processing and its applications, Manchester, pp 736–739

• Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2): 257–286

  Google Scholar 

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

  Google Scholar 

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

• Samrajya P, Lakshmi M, Hanmandlu, Swaroop A (2006) Segmentation of cursive handwritten words using Hypergraph, 1-4, TENCON, IEEE region 10 Conference

• Sayre KM (1973) Machine recognition of handwritten words: a project report. Pattern Recognit 5: 213–228

  Google Scholar 

• Scagliola C, Nicchiotti G (2000) Enhancing cursive word recognition performance by integration of all the available information. In: Proceedings of 7th international workshop on frontiers in handwriting recognition, Amsterdam, pp 363–372

• Schambach M-P (2005) Fast script word recognition with very large vocabulary. In: Proceedings of the 8th international conference on document analysis and recognition, pp 9–13

• Senior W, Robinson AJ (2002) An off-line cursive handwriting recognition system. IEEE Trans Pattern Anal Mach Intell 20(3): 309–321

  Google Scholar 

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

  MATH  Google Scholar 

• Sin BK, Kim JH (1997) Ligature modeling for online cursive script recognition. IEEE Trans Pattern Anal Mach Learn 19(6): 623–633

  Google Scholar 

• Sinha RMK, Prasada B, Houle G, Sabourin M (1993) Hybrid contextual text recognition with string matching. IEEE Trans Pattern Anal Mach Intell 15: 915–925

  Google Scholar 

• Steinherz T, Rivlin E, Intrator N (1999) Off-line cursive script word recognition—a survey. Int J Document Anal Recognit 2: 90–110

  Google Scholar 

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

• Suen CY, Berthod M, Mori S (1980) Automatic recognition of handprinted characters—the state of the art. Proc IEEE 68: 469–487

  Google Scholar 

• Suen CY, Legault R, Nadal C, Cheriet M, Lam L (1993) Building a new generation of handwriting recognition systems. Pattern Recognit Lett 14: 305–315

  Google Scholar 

• Takahashi T, Griffin (1993) Recognition enhancement by linear tournament verification. In: Proceedings of 2nd international conference on document analysis and recognition, pp 585–588

• Tappert CC, Suen CY, Wakahara T (1990) The state of the art in on-line handwriting recognition. IEEE Trans Pattern Anal Mach Intell 12(8): 787–793

  Google Scholar 

• Tay YH (2002) Off-line handwriting recognition using artificial neural network and hidden Morkov Model. PhD thesis, p 78

• Tay YH, Khalid M, Yusof R, Gaudin CV (2003) Off-line cursive handwriting recognition system based on hybrid Markov model and neural networks. In: Proceedings of IEEE international symposium on computational intelligence in robotics and automation, Kobe, pp 1190–1195

• Tomoyuki H, Takuma A, Bunpei I (2007) An analytic word recognition algorithm using a posteriori probability. In: Proceedings of the 9th international conference on document analysis and recognition, vol 2, pp 669–673

• Trier OD, Jain AK, Taxt T (1996) Feature extraction methods for character recognition—a Survey. Pattern Recognit 29(4): 641–662

  Google Scholar 

• Tucker ND, Evans FC (1974) A two-step strategy for character recognition using geometrical moments. In: Proceedings of the 2nd international conference on pattern recognition, pp 223–225

• Ullmann JR (1969) Experiments with the n-tuple method of pattern recognition. IEEE Trans Comput 18: 1135–1137

  MATH  Google Scholar 

• Valentini, Dietterich TG (2002) Bias-Variance Analysis and Ensembles of SVM. 3rd international workshop on multiple classifier systems. 222-231

• 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 4th IASTED international conference on signal processing, pattern recognition and applications, pp 197–202

• Varga T, Bunke H (2003) Generation of synthetic training data for an HMM-based handwriting recognition system. In: Proceedings of the 7th international conference on document analysis and recognition, Edinburgh, pp 618–622

• Veloso LR, Sousa RP, De, Carvalho JM (2000) Morphological cursive word segmentation. In: Symposium on computer graphics and image processing, 2000. XIII, Brazilian, pp 337–342

• 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 (2003) A contour code feature based segmentation for handwriting recognition. In: Proceedings of 7th international conference on document analysis and recognition (ICDAR’03), pp 1203–1207

• Verma B, Blumenstein M (1996) An intelligent neural system for a robot to recognize printed and handwritten postal addresses. In: Proceedings of 4th IASTED international conference on robotics and manufacturing, IASTED RM’96, Hawaii, pp 80–84

• Verma B, Gader P (2000) Fusion of multiple handwritten word recognition techniques. In: Neural networks for signal processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, vol 2, pp 926–934

• Verma B, Blumenstein M, Kulkarni S (1998) Recent achievements in off-line handwriting recognition systems. In: Proceedings of the 2nd international conference on computational intelligence and multimedia applications, (ICCIMA ‘98), Gippsland, pp 27–33

• Verma B, Gader P, Chen W (2001) Fusion of multiple handwritten word recognition techniques. Pattern Recognit Lett 22(9): 991–998

  MATH  Google Scholar 

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

  Google Scholar 

• Viard-Gaudin C, Lallican P-M, Knerr S (2005) Recognition-directed recovering of temporal information from handwriting images. Pattern Recognit Lett 26(16): 2537–2548

  Google Scholar 

• Vinciarelli A (2002) A survey on off-line cursive word recognition. Pattern Recognit 35(7): 1433–1446

  MATH  Google Scholar 

• Vuurpijl L, Schomaker L, Van M (2003) Architectures for detecting and solving conflicts: two-stage classification and support vector classifiers. Int J Document Anal Recognit 5(4): 213–223

  Google Scholar 

• Wang X, Ding X, Liu C (2005) Gabor filters based feature extraction for character recognition. Pattern Recognit 38(3): 369–379

  MathSciNet  MATH  Google Scholar 

• Xiao X, Leedham G (2000) Knowledge-based English cursive script segmentation. Pattern Recognit Lett 21: 945–954

  Google Scholar 

• Xu Q, Lam L, Suen CY (2003) Automatic segmentation and recognition system for handwritten dates on Canadian bank cheque. In: Fairhurst M, Downton A (eds) Proceedings of the 7th international conference on document analysis and recognition, pp 704–709

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

  Google Scholar 

• Yanikoglu B, Sandon PA (1998) Segmentation of off-line cursive handwriting using linear programming. Pattern Recognit 31: 1825–1833

  Google Scholar 

• Zhang P, Bui TD, Suen CY (2007) A Novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognit 40(12): 3415–3429

  MATH  Google Scholar 

• Zhou J, Gan Q, Krzyyzak A, Suen C-Y (2000) Recognition and verification of touching handwritten numerals. In: 7th International workshop on frontiers of handwriting recognition, pp 179–188

• Zimmermann M, Bunke H (2002) Hidden Markov model length optimization for handwriting recognition systems. In: International workshop on frontiers in handwriting recognition, Niagara-on-the-Lakes, pp 369–374

Download references