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Published in: International Journal of Machine Learning and Cybernetics 2/2013

01-04-2013 | Original Article

Effects of artificially intelligent tools on pattern recognition

Authors: Tanzila Saba, Amjad Rehman

Published in: International Journal of Machine Learning and Cybernetics | Issue 2/2013

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Abstract

Pattern recognition is classification process that attempts to assign each input value to one of a given set of classes. The process of pattern recognition in the state of art has been achieved either by training of artificially intelligent tools or using heuristic rule based approaches. The objective of this paper is to provide a comparative study between artificially trained and heuristics rule based techniques employed for pattern recognition in the state of the art focused on script pattern recognition. It is observed that mainly there are two categories of script pattern recognition techniques. First category involves assistance of artificial intelligent learning and next, is based on heuristic-rules for cursive script pattern segmentation/recognition. Accordingly, a detailed critical study is performed that focuses on size of training/testing data and implication of artificial learning on script pattern recognition accuracy. Moreover, the techniques are described in details that are employed to identify character patterns. Finally, performances of different techniques on benchmark database are compared regarding pattern recognition accuracy, error rate, single or multiple classifiers being employed. Problems that still persist are also highlighted and possible directions are set.

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Literature
1.
go back to reference Rehman A, Kurniawan F, Dzulkifli M (2009) Neuro-heuristic approach for segmenting cursive handwritten words. Int J Inf Process 3(2):37–46 Rehman A, Kurniawan F, Dzulkifli M (2009) Neuro-heuristic approach for segmenting cursive handwritten words. Int J Inf Process 3(2):37–46
2.
go back to reference Rehman A, Dzulkifli M (2008) A simple segmentation approach for unconstrained cursive handwritten words in conjunction of neural network. Int J Image Process 2(3):29–35 Rehman A, Dzulkifli M (2008) A simple segmentation approach for unconstrained cursive handwritten words in conjunction of neural network. Int J Image Process 2(3):29–35
3.
go back to reference Blumenstein M, Verma B (1999) Neural-based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database. Bangalore, India, pp 281–284 Blumenstein M, Verma B (1999) Neural-based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database. Bangalore, India, pp 281–284
4.
go back to reference Blumenstein M, Verma B (1999) A new segmentation algorithm for handwritten word recognition. Int Jt Conf Neural Netw (IJCNN ′99) 4:2893–2898 Blumenstein M, Verma B (1999) A new segmentation algorithm for handwritten word recognition. Int Jt Conf Neural Netw (IJCNN ′99) 4:2893–2898
5.
go back to reference Bozinovic RM, Srihari SN (1989) Off-line cursive script word recognition. IEEE Trans Pattern Anal Mach Intell 11:68–83CrossRef Bozinovic RM, Srihari SN (1989) Off-line cursive script word recognition. IEEE Trans Pattern Anal Mach Intell 11:68–83CrossRef
6.
go back to reference Wang X, Chena A, Feng H (2011) Upper integral network with extreme learning mechanism. Neurocomputing 74(16):2520–2525CrossRef Wang X, Chena A, Feng H (2011) Upper integral network with extreme learning mechanism. Neurocomputing 74(16):2520–2525CrossRef
7.
go back to reference Saba T, Sulong G, Rahim S, Rehman A (2010) On the segmentation of multiple touched characters: a heuristics approach. Lecturer Notes in Computer Science (LNCS). Springer, Berlin, p 540 Saba T, Sulong G, Rahim S, Rehman A (2010) On the segmentation of multiple touched characters: a heuristics approach. Lecturer Notes in Computer Science (LNCS). Springer, Berlin, p 540
8.
go back to reference Rehman A, Kurniawan F, Dzulkifli M (2008) Off-line cursive handwriting segmentation, a heuristic rule-based approach. J Inst Math Comput Sci (Computer Science Series) Kolkata, India, vol. 19(2), pp 135–139 Rehman A, Kurniawan F, Dzulkifli M (2008) Off-line cursive handwriting segmentation, a heuristic rule-based approach. J Inst Math Comput Sci (Computer Science Series) Kolkata, India, vol. 19(2), pp 135–139
9.
go back to reference Shafry R, 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 Shafry R, 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
10.
go back to reference Wang XZ, Zhai JH, Lu SX (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202MathSciNetMATHCrossRef Wang XZ, Zhai JH, Lu SX (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202MathSciNetMATHCrossRef
11.
go back to reference Kurniawan F, Rehman A, Dzulkifli M (2009) Contour vs. non-contour based word segmentation from handwritten text lines. An experimental analysis. Int J Digit Content Technol Appl 3(2):127–131 Kurniawan F, Rehman A, Dzulkifli M (2009) Contour vs. non-contour based word segmentation from handwritten text lines. An experimental analysis. Int J Digit Content Technol Appl 3(2):127–131
12.
go back to reference Rehman A, Saba T (2011) Performance analysis of segmentation approach for cursive handwritten word recognition on benchmark database. Digit Signal Process 21:486–490CrossRef Rehman A, Saba T (2011) Performance analysis of segmentation approach for cursive handwritten word recognition on benchmark database. Digit Signal Process 21:486–490CrossRef
13.
go back to reference Verma B, Gader P (2000) Fusion of multiple handwritten word recognition techniques. Neural networks for signal processing. In: Proceedings of IEEE signal processing society workshop, vol 2, pp 926–934 Verma B, Gader P (2000) Fusion of multiple handwritten word recognition techniques. Neural networks for signal processing. In: Proceedings of IEEE signal processing society workshop, vol 2, pp 926–934
16.
go back to reference Saba T, Rehman A, Elarbi-Boudihir M (2011) Methods and strategies on off-line cursive touched characters segmentation: a directional review. Artif Intell Rev. doi:10.1007/s10462-011-9271-5 Saba T, Rehman A, Elarbi-Boudihir M (2011) Methods and strategies on off-line cursive touched characters segmentation: a directional review. Artif Intell Rev. doi:10.​1007/​s10462-011-9271-5
17.
go back to reference Rehman A, Mohammad D, Sulong G, Saba T (2009) Simple and effective techniques for core zone detection and slant correction in script recognition. In: IEEE international conference on signal and image processing applications (ICSIPA’09), pp 15–20 Rehman A, Mohammad D, Sulong G, Saba T (2009) Simple and effective techniques for core zone detection and slant correction in script recognition. In: IEEE international conference on signal and image processing applications (ICSIPA’09), pp 15–20
18.
go back to reference Rehman A, Saba T, Sulong G (2010) An intelligent approach to image denoising. J Theor Appl Inf Technol 17(1):32–36 Rehman A, Saba T, Sulong G (2010) An intelligent approach to image denoising. J Theor Appl Inf Technol 17(1):32–36
19.
go back to reference Rehman A, Dzulkifli M, Kurniawan F (2008) Line and skew removal from off-line cursive handwritten words. Int J Res (Science) 24(2):28–33 Rehman A, Dzulkifli M, Kurniawan F (2008) Line and skew removal from off-line cursive handwritten words. Int J Res (Science) 24(2):28–33
20.
go back to reference Lecce VD, Dimauro A, Guerriero, Impedovo S, Pirlo G, Salzo A (2000) A new hybrid approach for legal amount recognition. In: Proceedings of 7th international workshop on Frontiers in handwriting recognition. Amsterdam, pp 199–208 Lecce VD, Dimauro A, Guerriero, Impedovo S, Pirlo G, Salzo A (2000) A new hybrid approach for legal amount recognition. In: Proceedings of 7th international workshop on Frontiers in handwriting recognition. Amsterdam, pp 199–208
22.
go back to reference Lecolinet E, Baret O (1994) Cursive word recognition: methods and strategies. fundamentals in handwriting recognition. In: Impedovo S (ed), NATO ASI Series F, vol 124. Springer, UK Lecolinet E, Baret O (1994) Cursive word recognition: methods and strategies. fundamentals in handwriting recognition. In: Impedovo S (ed), NATO ASI Series F, vol 124. Springer, UK
23.
go back to reference Rehman A, Saba T (2011) Document skew estimation and correction: analysis of techniques, common problems and possible solutions. Appl Artif Intell 25(9):769–787CrossRef Rehman A, Saba T (2011) Document skew estimation and correction: analysis of techniques, common problems and possible solutions. Appl Artif Intell 25(9):769–787CrossRef
24.
go back to reference Rehman A, Kurniawan F, Mohammad D (2009) 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 Rehman A, Kurniawan F, Mohammad D (2009) 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
25.
go back to reference Saba T, Rehman A, Sulong G (2010) Non-linear segmentation of touched roman characters based on genetic algorithm. Int J Comput Sci Eng 2(6):2167–2172 Saba T, Rehman A, Sulong G (2010) Non-linear segmentation of touched roman characters based on genetic algorithm. Int J Comput Sci Eng 2(6):2167–2172
26.
go back to reference Saba T, Rehman A, Sulong G (2010) Improved statistical features for cursive character recognition. Int J Innov Comput Inform Control 7(9):5211–5224 Saba T, Rehman A, Sulong G (2010) Improved statistical features for cursive character recognition. Int J Innov Comput Inform Control 7(9):5211–5224
27.
go back to reference Wang XZ, Yeung DS, Tsang ECC (2001) A comparative study on heuristic algorithms for generating fuzzy decision trees. IEEE Trans Syst Man Cybern Part B 31(2):215–226CrossRef Wang XZ, Yeung DS, Tsang ECC (2001) A comparative study on heuristic algorithms for generating fuzzy decision trees. IEEE Trans Syst Man Cybern Part B 31(2):215–226CrossRef
28.
go back to reference Saba T, Rehman A, Sulong G (2010) Cursive script segmentation with neural confidence. Int J Innov Comput Inf Control 7(8):4955–4964 Saba T, Rehman A, Sulong G (2010) Cursive script segmentation with neural confidence. Int J Innov Comput Inf Control 7(8):4955–4964
29.
go back to reference Binu Chacko P, Vimal Krishnan VR, Raju G, Babu Anto P (2011) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cyber. doi:10.1007/s13042-011-0049-5 Binu Chacko P, Vimal Krishnan VR, Raju G, Babu Anto P (2011) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cyber. doi:10.​1007/​s13042-011-0049-5
30.
go back to reference Castillo JJ (2011) A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment. Int J Mach Learn Cyber 2(3):177–189CrossRef Castillo JJ (2011) A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment. Int J Mach Learn Cyber 2(3):177–189CrossRef
31.
go back to reference Biggio B, Fumera G, Roli F (2010) Multiple classifier systems for robust classifier design in adversarial environments. Int J Mach Learn Cybern 1(1–4):27–41CrossRef Biggio B, Fumera G, Roli F (2010) Multiple classifier systems for robust classifier design in adversarial environments. Int J Mach Learn Cybern 1(1–4):27–41CrossRef
32.
go back to reference Yanikoglu B, Sandon PA (1998) Segmentation of off-line cursive handwriting using linear programming. Pattern Recogn 31:1825–1833CrossRef Yanikoglu B, Sandon PA (1998) Segmentation of off-line cursive handwriting using linear programming. Pattern Recogn 31:1825–1833CrossRef
33.
go back to reference Verma B (2002) A contour character extraction approach in conjunction with a neural confidence fusion technique for the segmentation of handwriting recognition. In: Proceedings of the 9th international conference on neural information processing, vol 5, pp 2459–2463 Verma B (2002) A contour character extraction approach in conjunction with a neural confidence fusion technique for the segmentation of handwriting recognition. In: Proceedings of the 9th international conference on neural information processing, vol 5, pp 2459–2463
34.
go back to reference Blumenstein M, Verma B (2001) Analysis of segmentation performance on the CEDAR benchmark database. In: Proceedings of sixth international conference on document analysis and recognition, pp 1142–1146. doi:10.1109/ICDAR.2001.953964 Blumenstein M, Verma B (2001) Analysis of segmentation performance on the CEDAR benchmark database. In: Proceedings of sixth international conference on document analysis and recognition, pp 1142–1146. doi:10.​1109/​ICDAR.​2001.​953964
35.
go back to reference 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 (SEAL ′04), Busan, Korea, SWA-8 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 (SEAL ′04), Busan, Korea, SWA-8
36.
go back to reference 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 (IJCNN ′04), Budapest, Hungary (CD-ROM Proceedings) 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 (IJCNN ′04), Budapest, Hungary (CD-ROM Proceedings)
37.
go back to reference Veloso LR, Sousa RP, De Carvalho JM (2000) Morphological cursive word segmentation. Symp Comput Graphics Image Process 3(2):337–343 Veloso LR, Sousa RP, De Carvalho JM (2000) Morphological cursive word segmentation. Symp Comput Graphics Image Process 3(2):337–343
38.
go back to reference Cheng CK, Blumenstein M (2005) The neural-based segmentation of cursive words using enhanced heuristics. In: Proceedings of 8th international conference on document analysis and recognition, vol 2, pp 650–654 Cheng CK, Blumenstein M (2005) The neural-based segmentation of cursive words using enhanced heuristics. In: Proceedings of 8th international conference on document analysis and recognition, vol 2, pp 650–654
39.
go back to reference Cheng CK, Blumenstein M (2005) 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, Australia, pp 56–59 Cheng CK, Blumenstein M (2005) 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, Australia, pp 56–59
40.
go back to reference Han K, Sethi IK (1995) Off-line cursive handwriting segmentation. In: Proceedings of third international conference on document analysis and recognition, vol. 2, pp 234–240 Han K, Sethi IK (1995) Off-line cursive handwriting segmentation. In: Proceedings of third international conference on document analysis and recognition, vol. 2, pp 234–240
41.
go back to reference Nicchiotti G, Scagliola C (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, Nijmegen: International Unipen Foundation, pp 499–504 Nicchiotti G, Scagliola C (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, Nijmegen: International Unipen Foundation, pp 499–504
42.
go back to reference Verma B (2003) A contour code feature based segmentation for handwriting recognition. In: Proceedings of the seventh international conference on document analysis and recognition (ICDAR), vol 2. IEEE computer society, Washington, DC, p 1203 Verma B (2003) A contour code feature based segmentation for handwriting recognition. In: Proceedings of the seventh international conference on document analysis and recognition (ICDAR), vol 2. IEEE computer society, Washington, DC, p 1203
43.
go back to reference Haron H, Shafry R, Rehman A, Saba T (2010) Curve length estimation using vertix chain code. Int J Comp Sci Eng 2(6):2110–2113 Haron H, Shafry R, Rehman A, Saba T (2010) Curve length estimation using vertix chain code. Int J Comp Sci Eng 2(6):2110–2113
44.
go back to reference Maragoudakis M, Kavallieratou E, Fakotakis N, Kokkinakis G (2003) An effective stochastic estimation of handwritten character segmentation bounds, ISAP 2003: competitive environment, renewable energy. Distributed Generation, Lemnos Maragoudakis M, Kavallieratou E, Fakotakis N, Kokkinakis G (2003) An effective stochastic estimation of handwritten character segmentation bounds, ISAP 2003: competitive environment, renewable energy. Distributed Generation, Lemnos
45.
go back to reference Kurniawan F, Rahim MSM, Daman D, Rehman A, Dzulkifli M, Mariyam S (2011) Region-based touched character segmentation in handwritten words. Int J Innov Comput Inf Control 7(6):3107–3120 Kurniawan F, Rahim MSM, Daman D, Rehman A, Dzulkifli M, Mariyam S (2011) Region-based touched character segmentation in handwritten words. Int J Innov Comput Inf Control 7(6):3107–3120
46.
go back to reference Haron H, Rehman A, Wulandhari LA, Saba T (2011) Improved vertex chain code algorithm for curve length estimation. J Comput Sci 7(5):736–743CrossRef Haron H, Rehman A, Wulandhari LA, Saba T (2011) Improved vertex chain code algorithm for curve length estimation. J Comput Sci 7(5):736–743CrossRef
Metadata
Title
Effects of artificially intelligent tools on pattern recognition
Authors
Tanzila Saba
Amjad Rehman
Publication date
01-04-2013
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 2/2013
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0082-z

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