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Unconstrained handwritten digit recognition using perceptual shape primitives

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Abstract

In this paper, we propose a new handwritten digit recognition method which works in a very similar way as human perception. The digit image boundary is decomposed into four salient visual primitives, namely closure, smooth curve, protrusion and straight segment by defining a set of external symmetry axis. Unlike the conventional algorithms, our low complexity shape decomposition method neither searches for curvature minima nor finds optimal parsing by using shortcut and convexity rules. Based on the spatial configuration of extracted primitives, the recognizer classifies a test digit image using a set of classification rules. The performance of our proposed recognition system is evaluated on five digit datasets of four popular scripts, Odia, Bangla, Arabic and English. The recognition accuracies on the ISI Kolkata Odia and Bangla, IITBBS Odia, CMATERdb Arabic and MNIST English digit datasets are found to be 99.02, 99.25, 99.66, 97.96 and 99.11%, respectively. The proposed method outperforms the existing recognition systems on both the Odia digit datasets and achieves comparable performance in other cases.

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Notes

  1. IITBBS Odia Handwriting Database: www.iitbbs.ac.in/profile.php/nbpuhan.

References

  1. Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522

    Article  Google Scholar 

  4. Khotanzad A, Yong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497

    Article  Google Scholar 

  5. Belkasim SO, Shridhar M, Ahmadi M (1991) Pattern recognition with moment invariants: a comparative study and new results. Pattern Recogn 24(2):1117–1138

    Article  Google Scholar 

  6. Roy K, Pal T, Pal U, Kimura F (2005) Oriya handwritten digit recognition system. In: Proceedings of IEEE 8th international conference on document analysis and recognition, ICDAR 2005, pp 770–774

  7. Pal U, Sharma N, Wakabayashi T, Kimura F (2007) Handwritten digit recognition of six popular Indian scripts. In: Proceedings of IEEE 9th international conference on document analysis and recognition, ICDAR 2007, pp 749–753

  8. Wen Y, Lu Y, Shi P (2007) Handwritten Bangla digit recognition system and its application to postal automation. Pattern Recogn 40(1):99–107

    Article  MATH  Google Scholar 

  9. Liu CL, Nakashima K, Sako H, Fujisawa H (2004) Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recogn 37(2):265–279

    Article  MATH  Google Scholar 

  10. Dash KS, Puhan NB, Panda G (2014) A hybrid feature and discriminant classifier for high accuracy Odia handwritten digit recognition. In: IEEE region 10 technical symposium, TENSYMP’14, pp 531–535

  11. Chen G, Bui TD (1999) Invariant Fourier-wavelet descriptor for pattern recognition. Pattern Recogn 32(7):1083–1088

    Article  Google Scholar 

  12. Bhattacharya U, Chaudhuri BB (2009) Handwritten digit databases of Indian scripts and multistage recognition of mixed digits. IEEE Trans Pattern Anal Mach Intell 31(3):444–457

    Article  Google Scholar 

  13. Dash KS, Puhan NB, Panda G (2014) Non-redundant stockwell transform based feature extraction for handwritten digit recognition. In: Proceedings of IEEE signal processing and communication (SPCOM’14), pp 1–4

  14. Trier ØD, Jain AK, Taxt T (1996) Feature extraction methods for character recognition-a survey. Pattern Recogn 29(4):641–662

    Article  Google Scholar 

  15. Jayadevan R, Kolhe SR, Patil PM, Pal U (2011) Offline recognition of Devanagari script: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):782–796

    Article  Google Scholar 

  16. Jain AK, Zhong Y, Lakshmanan S (1996) Object matching using deformable templates. IEEE Trans Pattern Anal Mach Intell 18(3):267–278

    Article  Google Scholar 

  17. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  18. Jain AK, Zongker D (1997) Representation and recognition of handwritten digits using deformable templates. IEEE Trans Pattern Anal Mach Intell 19(12):1386–1390

    Article  Google Scholar 

  19. Teow LN, Loe KF (2002) Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recogn 35(11):2355–2364

    Article  MATH  Google Scholar 

  20. Vamvakas G, Gatos B, Perantonis SJ (2010) Handwritten character recognition through two-stage foreground sub-sampling. Pattern Recogn 43(8):2807–2816

    Article  MATH  Google Scholar 

  21. Shi D, Gunn SR, Damper RI (2003) Handwritten Chinese radical recognition using nonlinear active shape models. IEEE Trans Pattern Anal Mach Intell 25(2):277–280

    Article  Google Scholar 

  22. Zhao C, Shi W, Deng Y (2005) A new Hausdorff distance for image matching. Pattern Recogn Lett 26(5):581–586

    Article  Google Scholar 

  23. Liu MY, Tuzel O, Veeraraghavan A, Chellappa R (2010) Fast directional chamfer matching. In: Proceedings of IEEE computer vision and pattern recognition (CVPR), pp 1696–1703

  24. Atallah MJ (2001) Faster image template matching in the sum of the absolute value of differences measure. IEEE Trans Image Process 10(4):659–663

    Article  MathSciNet  MATH  Google Scholar 

  25. Ciresan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: Proceedings of IEEE computer vision and pattern recognition (CVPR), pp 3642–3649

  26. Blake R, Sekuler R (2005) Perception. McGraw-Hill, New York

    Google Scholar 

  27. Hoffman DD, Singh M (1997) Salience of visual parts. Cognition 63(1):29–78

    Article  Google Scholar 

  28. Pilu M, Fisher RB (1997) Model-driven grouping and recognition of generic object parts from single images. Robot Auton Syst 21(1):107–122

    Article  Google Scholar 

  29. Zhu L, Chen Y, Torralba A, Freeman W, Yuille A (2010) Part and appearance sharing: Recursive compositional models for multi-view. In: Proceedings of IEEE computer vision and pattern recognition (CVPR), pp 1919–1926

  30. Basri R, Costa L, Geiger D, Jacobs D (1998) Determining the similarity of deformable shapes. Vis Res 38(15):2365–2385

    Article  Google Scholar 

  31. Lien JM, Amato NM (2004) Approximate convex decomposition of polygons. In: Proceedings of ACM 20th annual symposium on Computational geometry, pp 17–26

  32. Lu Y, Lien JM, Ghosh M, Amato NM (2012) α-decomposition of polygons. Comput Graph 36(5):466–476

    Article  Google Scholar 

  33. Latecki LJ, Lakämper R (1999) Convexity rule for shape decomposition based on discrete contour evolution. Comput Vis Image Underst 73(3):441–454

    Article  Google Scholar 

  34. Liu H, Liu W, Latecki LJ (2010) Convex shape decomposition. In: Proceedings of IEEE computer vision and pattern recognition (CVPR), pp 97–104

  35. Ren Z, Yuan J, Li C, Liu W (2011) Minimum near-convex decomposition for robust shape representation. In: Proceedings of IEEE international conference on computer vision (ICCV), pp 303–310

  36. Jiang T, Dong Z, Ma C, Wang Y (2013) Toward perception-based shape decomposition. In: Proceedings of Asian conference on computer vision (ACCV). Springer, Berlin, pp 188–201

  37. Ma C, Dong Z, Jiang T, Wang Y, Gao W (2013) A method of perceptual-based shape decomposition. In: Proceedings of IEEE international conference on computer vision (ICCV), pp 873–880

  38. Ghosh M, Amato NM, Lu Y, Lien J-M (2013) Fast approximate convex decomposition using relative concavity. Comput Aided Des 45(2):494–504

    Article  MathSciNet  Google Scholar 

  39. Luo L, Shen C, Liu X, Zhang C (2015) A computational model of the short-cut rule for 2D shape decomposition. IEEE Trans Image Process 24(1):273–283

    Article  MathSciNet  Google Scholar 

  40. Biederman I (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev 94(2):115–147

    Article  Google Scholar 

  41. Kaick OV, Fish N, Kleiman Y, Asafi S, Cohen-Or D (2014) Shape segmentation by approximate convexity analysis. ACM Trans Graph (TOG) 34(1):4

    Article  Google Scholar 

  42. Hoffman DD, Richards WA (1984) Parts of recognition. Cognition 18(1):65–96

    Article  Google Scholar 

  43. Singh M, Seyranian GD, Hoffman DD (1999) Parsing silhouettes: the short-cut rule. Percept Psychophys 61(4):636–660

    Article  Google Scholar 

  44. Singh M, Hoffman DD (2001) 13-Part-based representations of visual shape and implications for visual cognition. Adv Psychol 130:401–459

    Article  Google Scholar 

  45. Gopalan R, Turaga P, Chellappa R (2010) Articulation-invariant representation of non-planar shapes. In: Proceedings of computer vision (ECCV). Springer, Berlin, pp 286–299\

  46. Rosin PL (2000) Shape partitioning by convexity. IEEE Trans Syst Man Cybern Part A 30(2):202–210

    Article  Google Scholar 

  47. De Winter J, Wagemans J (2006) Segmentation of object outlines into parts: a large-scale integrative study. Cognition 99(3):275–325

    Article  Google Scholar 

  48. Macrini D, Siddiqi K, Dickinson S (2008) From skeletons to bone graphs: medial abstraction for object recognition. In: Proceedings of IEEE computer vision and pattern recognition (CVPR), pp 1–8

  49. Siddiqi K, August J, Zucker SW (1999) Ligature instabilities in the perceptual organization of shape. Comput Vis Image Underst 76(3):231–243

    Article  Google Scholar 

  50. Zhu SC (1999) Stochastic jump-diffusion process for computing medial axes in Markov random fields. IEEE Trans Pattern Anal Mach Intell 21(11):1158–1169

    Article  MathSciNet  Google Scholar 

  51. Siddiqi K, Kimia BB (1995) Parts of visual form: computational aspects. IEEE Trans Pattern Anal Mach Intell 17(3):239–251

    Article  Google Scholar 

  52. Mi X, DeCarlo D (2007) Separating parts from 2D shapes using relatability. In: Proceedings of IEEE 11th international conference on computer vision (ICCV), pp 1–8

  53. Pitas I, Venetsanopoulos AN (1990) Morphological shape decomposition. IEEE Trans Pattern Anal Mach Intell 12(1):38–45

    Article  Google Scholar 

  54. Xu J (2001) Morphological decomposition of 2-D binary shapes into convex polygons: a heuristic algorithm. IEEE Trans Image Process 10(1):61–71

    Article  MATH  Google Scholar 

  55. Wang D, Haese-Coat V, Ronsin J (1995) Shape decomposition and representation using a recursive morphological operation. Pattern Recogn 28(11):1783–1792

    Article  Google Scholar 

  56. Vanderheydt L, Dom F, Oosterlinck A, Van den Berghe H (1981) Two-dimensional shape decomposition using fuzzy subset theory applied to automated chromosome analysis. Pattern Recogn 13(2):147–157

    Article  Google Scholar 

  57. Draper NR, Smith H, Pownell E (1966) Applied regression analysis, vol 3. Wiley, New York

    Google Scholar 

  58. Gonzalez RC (2009) Digital image processing. Pearson Education India, Prentice-Hall

    Google Scholar 

  59. Bhattacharya U, Chaudhuri BB (2005) Databases for research on recognition of handwritten characters of Indian scripts. In: Proceedings of IEEE 8th international conference on document analysis and recognition (ICDAR), pp 789–793

  60. Bhowmik TK, Parui SK, Bhattacharya U, Shaw B (2006) An HMM based recognition scheme for handwritten Oriya digits. In: Proceedings of IEEE 9th international conference on information technology, pp 105–110

  61. Dash KS, Puhan NB, Panda G (2015) Gestalt configural superiority effect: a complexity paradigm for handwritten digit recognition. In: Proceedings of IEEE eighth international conference on advances in pattern recognition (ICAPR), pp 1–6

  62. Roy K, Chaudhuri C, Pal U Kundu M (2005) A study on the effect of varying training set sizes on recognition performance with handwritten bangla digits. In: Proceedings of IEEE annual INDICON, pp 570–574

  63. Pal U, Belaïd A (2006) A system for Bangla handwritten digit recognition. IETE J Res 52(1):27–34

    Article  Google Scholar 

  64. Liu CL, Suen CY (2009) A new benchmark on the recognition of handwritten Bangla and Farsi digit characters. Pattern Recogn 42(12):3287–3295

    Article  MATH  Google Scholar 

  65. Purkait P, Chanda B (2010) Off-line recognition of hand-written bengali digits using morphological features. In: Proceedings of IEEE international conference on frontiers in handwriting recognition (ICFHR), pp 363–368

  66. Basu S, Das N, Sarkar R, Kundu M, Nasipuri M, Basu DK (2010) A novel framework for automatic sorting of postal documents with multi-script address blocks. Pattern Recogn 43(10):3507–3521

    Article  MATH  Google Scholar 

  67. Wen Y, He L (2012) A classifier for Bangla handwritten digit recognition. Expert Syst Appl 39(1):948–953

    Article  Google Scholar 

  68. Ciresan DC, Meier U, Masci J, Maria Gambardella L, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. In: Proceedings of international joint conference on artificial intelligence (IJCAI), vol 22, pp 1237–1242

  69. Jarrett K, Kavukcuoglu K, Ranzato M, LeCun Y (2009) What is the best multi-stage architecture for object recognition? In: Proceedings of IEEE 12th international conference on computer vision (ICCV), pp 2146–2153

  70. Singh S, Gupta A, Efros AA (2012) Unsupervised discovery of mid-level discriminative patches. In: Proceedings of computer vision–ECCV. Springer, Berlin, pp 73–86

  71. Doersch C, Singh S, Gupta A, Sivic J, Efros AA (2012) What makes Paris look like Paris? ACM Trans Graph 31(4):101

    Article  Google Scholar 

  72. Suen CY, Guo J, Li ZC (1992) Analysis and recognition of alphanumeric handprints by parts. In: Proceedings of IEEE 11th international conference on pattern recognition, pp 338–341

  73. Li CZ, Li HJ, Suen CY, Wang HQ, Liao SY (2002) Recognition of handwritten characters by parts with multiple orientations. Math Comput Model 35(3):441–479

    Article  MATH  Google Scholar 

  74. Dash KS, Puhan NB, Panda G (2015) Handwritten numeral recognition using non-redundant Stockwell transform and bio-inspired optimal zoning. IET Image Process 9(10):874–882

    Article  Google Scholar 

  75. Das N, Mollah AF, Sarkar R, Basu S (2010) A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi-Layer Perceptron. Preprint arXiv:1003.1894

  76. Das N, Reddy JM, Sarkar R et al (2012) A statistical–topological feature combination for recognition of handwritten numerals. Appl Soft Comput 12(8):2486–2495

    Article  Google Scholar 

  77. Cecotti H (2016) Active graph based semi-supervised learning using image matching: application to handwritten digit recognition. Pattern Recogn Lett 73:76–82

    Article  Google Scholar 

  78. Liu CL, Nakashima K, Sako H, Fujisawa H (2003) Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recogn 36(10):2271–2285

    Article  MATH  Google Scholar 

  79. http://yann.lecun.com/exdb/mnist/. Accessed 10 Sept 2016

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Authors and Affiliations

  1. School of Electrical Sciences, Indian Institute of Technology (IIT) Bhubaneswar, Bhubaneswar, 751013, India

    Kalyan S. Dash, Niladri B. Puhan & Ganapati Panda

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  1. Kalyan S. Dash
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  2. Niladri B. Puhan
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  3. Ganapati Panda
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Correspondence to Kalyan S. Dash.

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Dash, K.S., Puhan, N.B. & Panda, G. Unconstrained handwritten digit recognition using perceptual shape primitives. Pattern Anal Applic 21, 413–436 (2018). https://doi.org/10.1007/s10044-016-0586-3

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