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A comparative study of graphic symbol recognition methods

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Abstract

From the very beginning of written scripts, contents of documents generally comprise of text, images, figures, graphs and graphic symbols. A graphic recognition system involves representation of graphic symbols, description of features extracted from the symbol and classification of the unknown symbols. Due to the wide range of symbols, no generalize technique is available that can recognize the symbol for all the application domains. this paper, we present an overview of the many models and methodologies available to symbol recognition for representation, description and classification. We provide a general survey of symbol recognition process, beginning with the basic definition of symbol, which is further classified into their types based on application areas. distinctive part of the survey is categorization of different symbol recognition methods into four categories i.e. statistical, structural, syntactical and hybrid methods, which is aimed to help potential researchers in exploring areas of research in the field of graphic symbol recognition.

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References

  1. Ablameyko S (1997) An introduction to interpretation of graphic images. SPIE Press, vol 27

  2. Ablameyko S, Bereishik V, Frantskevich O, Homenko M, Paramonova N (1998) Knowledge-based recognition of crosshatched areas in engineering drawings. In: Advances in Pattern Recognition. Springer, pp 460–467

  3. Adam S, Ogier J-M, Cariou C, Gardes J, Mullot R, Lecourtier Y (2000) Combination of invariant pattern recognition primitives on technical documents. In: Graphics Recognition Recent Advances. Springer, pp 238–245

  4. Ah-Soon C, Tombre K (2001) Architectural symbol recognition using a network of constraints. Pattern Recogn Lett 22(2):231–248

    MATH  Google Scholar 

  5. Almazán J, Fornés A, Valveny E (2012) A non-rigid appearance model for shape description and recognition. Pattern Recogn 45(9):3105–3113

    Google Scholar 

  6. Anquetil É, Coüasnon B, Dambreville F (2000) A symbol classifier able to reject wrong shapes for document recognition systems. In: Graphics Recognition Recent Advances. Springer, pp 209–218

  7. Antoine D, Collin S, Tombre K (1992) Analysis of technical documents: The redraw system. In: Structured document image analysis. Springer, pp 385–402

  8. Aoki Y, Shio A, Arai H, Odaka K (1996) A prototype system for interpreting hand-sketched floor plans. In: 1996., Proceedings of the 13th International Conference on Pattern Recognition. IEEE, vol 3, pp 747–751

  9. Arbter K, Snyder WE, Burkhardt H, Hirzinger G (1990) Application of affine-invariant fourier descriptors to recognition of 3-d objects. IEEE Trans Pattern Anal Mach Intell 12(7):640–647

    Google Scholar 

  10. Arias JF, Lai CP, Chandran S, Kasturi R, Chhabra A (1993) Interpretation of telephone system manhole drawings. In: 1993., Proceedings of the Second International Conference on Document Analysis and Recognition. IEEE, pp 365–368

  11. Armand J-P (1993) Musical score recognition: a hierarchical and recursive approach. In: 1993., Proceedings of the Second International Conference on Document Analysis and Recognition. IEEE, pp 906–909

  12. Attalla E, Siy P (2005) Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching. Pattern Recogn 38 (12):2229–2241

    Google Scholar 

  13. Bailey RR, Srinath M (1996) Orthogonal moment features for use with parametric and non-parametric classifiers. IEEE Trans Pattern Anal Mach Intell 18(4):389–399

    Google Scholar 

  14. Ballard DH (1981) Generalizing the hough transform to detect arbitrary shapes. Pattern Recogn 13(2):111–122

    MATH  Google Scholar 

  15. Belkasim SO, Shridhar M, Ahmadi M (1991) Pattern recognition with moment invariants: a comparative study and new results. Pattern Recogn 24(12):1117–1138

    Google Scholar 

  16. Blostein D, Baird HS (1992) A critical survey of music image analysis. In: Structured Document Image Analysis. Springer, pp 405–434

  17. Blostein D (1995) General diagram-recognition methodologies. In: Graphics Recognition Methods and Applications. Springer, pp 106–122

  18. Boatto L, Consorti V, Del Buono M, Di Zenzo S, Eramo V, Esposito A, Melcarne F, Meucci M, Morelli A, Mosciatti M et al (1992) An interpretation system for land register maps. Computer 25(7):25–33

    Google Scholar 

  19. Boumaiza A, Tabbone S (2012) Impact of a codebook filtering step on a galois lattice structure for graphics recognition. In: 2012 21st International Conference on Pattern Recognition (ICPR). IEEE, pp 278–281

  20. Boumaiza A, Tabbone S (2012) Symbol recognition using a galois lattice of frequent graphical patterns. In: 2012 10th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, pp 165–169

  21. Bunke H (1982) Automatic interpretation of lines and text in circuit diagrams. In: Pattern Recognition Theory and Applications. Springer, pp 297–310

  22. Bunke H (1982) Experience with several methods for the analysis of schematic diagrams. In: Proceedings of 6th International Conference on Pattern Recognition, pp 710–712

  23. Bunke H (1982) Attributed programmed graph grammars and their application to schematic diagram interpretation. IEEE Trans Pattern Anal Mach Intell 6:574–582

    MATH  Google Scholar 

  24. Bunke H, Sanfeliu A (1990) Syntactic and structural pattern recognition, theory and applications. World Scientific, vol 7

  25. Bunke H, Messmer BT (1995) Efficient attributed graph matching and its application to image analysis. In: Image Analysis and Processing. Springer, pp 44–55

  26. Bunke H, Messmer BT (1997) Recent advances in graph matching. Int J Pattern Recognit Artif Intell 11(01):169–203

    Google Scholar 

  27. Bunke H, Riesen K (2011) Recent advances in graph-based pattern recognition with applications in document analysis. Pattern Recogn 44(5):1057–1067

    MATH  Google Scholar 

  28. Celik M, Yanikoglu B (2011) Probabilistic mathematical formula recognition using a 2d context-free graph grammar. In: 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, pp 161–166

  29. Cesarini F, Francesconi E, Gori M, Marinai S, Sheng J, Soda G (1997) A neural-based architecture for spot-noisy logo recognition. In: 1997., Proceedings of the Fourth International Conference on Document Analysis and Recognition. IEEE, vol 1, pp 175–179

  30. Chang M-T, Chen S-Y (2001) Deformed trademark retrieval based on 2d pseudo-hidden markov model. Pattern Recogn 34(5):953–967

    MATH  Google Scholar 

  31. Chaudhuri BB, Garain U (2000) An approach for recognition and interpretation of mathematical expressions in printed document. Pattern Anal Appl 3(2):120–131

    Google Scholar 

  32. Chhabra A (1998) Graphic symbol recognition: An overview. In: Tombre K, Chhabra A (eds) Graphics Recognition Algorithms and Systems, ser. Lecture Notes in Computer Science, vol 1389. Springer, Berlin, pp 68?-79

  33. Chiang Y-Y, Leyk S, Knoblock CA (2014) A survey of digital map processing techniques. ACM Comput Surv (CSUR) 47(1):1

    Google Scholar 

  34. Chong C-W, Raveendran P, Mukundan R (2003) A comparative analysis of algorithms for fast computation of zernike moments. Pattern Recogn 36(3):731–742

    MATH  Google Scholar 

  35. Collin S, Colnet D (1994) Syntactic analysis of technical drawing dimensions. Int J Pattern Recognit Artif Intell 8(05):1131–1148

    Google Scholar 

  36. Conte D, Foggia P, Sansone C, Vento M (2004) Thirty years of graph matching in pattern recognition. Int J Pattern Recogn Artif Intell 18(03):265–298

    Google Scholar 

  37. Cordella LP, Vento M (2000) Symbol recognition in documents: a collection of techniques?. Int J Doc Anal Recogn 3(2):73–88

    Google Scholar 

  38. Coustaty M, Bertet K, Visani M, Ogier J-M (2011) A new adaptive structural signature for symbol recognition by using a galois lattice as a classifier. IEEE Trans Syst Man Cybern Part B: Cybern 41(4):1136–1148

    Google Scholar 

  39. De P, Mandal S, Bhowmick P, Das A (2015) Askme: adaptive sampling with knowledge-driven vectorization of mechanical engineering drawings. International Journal on Document Analysis and Recognition (IJDAR) 19(1):11–29

    Google Scholar 

  40. de las Heras L-P, Ahmed S, Liwicki M, Valveny E, Sánchez G (2014) Statistical segmentation and structural recognition for floor plan interpretation. Int J Doc Anal Recogn (IJDAR) 17(3):221–237

    Google Scholar 

  41. Deans SR (2007) The Radon transform and some of its applications. Courier Corporation

  42. den Hartog J, Ten Kate T, Gerbrands J (1996) Knowledge-based segmentation for automatic map interpretation. In: Graphics Recognition Methods and Applications. Springer, pp 159–178

  43. Doermann D, Rivlin E, Weiss I (1996) Applying algebraic and differential invariants for logo recognition. Mach Vis Appl 9(2):73–86

    Google Scholar 

  44. Dori D (1989) A syntactic/geometric approach to recognition of dimensions in engineering machine drawings. Comput Vis Graph Image Process 47(3):271–291

    Google Scholar 

  45. Dori D (1997) Orthogonal zig-zag: an algorithm for vectorizing engineering drawings compared with hough transform. Adv Eng Softw 28(1):11–24

    Google Scholar 

  46. Dosch P, Masini G, Tombre K (2000) Improving arc detection in graphics recognition. In: Icpr. IEEE, pp 2243

  47. Dosch P, Tombre K, Ah-Soon C, Masini G (2000) A complete system for the analysis of architectural drawings. Int J Doc Anal Recognit 3(2):102–116

    Google Scholar 

  48. Dosch P, Lladós J (2004) Vectorial signatures for symbol discrimination. In: Graphics Recognition. Recent Advances and Perspectives. Springer, pp 154–165

  49. Duda RO, Hart PE et al (1973) Pattern classification and scene analysis, vol 3. Wiley, New York

    MATH  Google Scholar 

  50. El-ghazal A, Basir O, Belkasim S (2007) A new shape signature for fourier descriptors. In: 2007. ICIP 2007. IEEE International Conference on Image Processing. IEEE, vol 1, pp I–161

  51. Escalera S, Fornés A, Pujol O, Lladós J, Radeva P (2011) Circular blurred shape model for multiclass symbol recognition. IEEE Trans Syst Man Cybern Part B: Cybern 41(2):497–506

    Google Scholar 

  52. Escalera S, Fornés A, Pujol O, Radeva P, Sánchez G, Lladós J (2009) Blurred shape model for binary and grey-level symbol recognition. Pattern Recogn Lett 30(15):1424–1433

    Google Scholar 

  53. Fahmy H, Blostein D (1992) A survey of graph grammars: Theory and applications. In: 1992. Vol. II. Conference B: Pattern Recognition Methodology and Systems, Proceedings., 11th IAPR International Conference on Pattern Recognition. IEEE, pp 294–298

  54. Fahmy H, Blostein D (1993) A graph grammar programming style for recognition of music notation. Mach Vis Appl 6(2-3):83–99

    Google Scholar 

  55. Fei T, Rui W, Qingxin D, Lei X New approach for oil-well symbol recognition in petroleum geological structure map. In: 2010 International Conference on Electrical and Control Engineering (ICECE). IEEE, 2010, pp 5357–5360

  56. Feng G, Viard-Gaudin C, Sun Z (2009) On-line hand-drawn electric circuit diagram recognition using 2d dynamic programming. Pattern Recogn 42(12):3215–3223

    MATH  Google Scholar 

  57. Foggia P, Percannella G, Vento M (2014) Graph matching and learning in pattern recognition in the last 10 years. Int J Pattern Recogn Artif Intell 28(01):1450001

    MathSciNet  Google Scholar 

  58. Foulds LR (2012) Graph theory applications. Springer Science & Business Media, Berlin

    Google Scholar 

  59. Francesconi E, Frasconi P, Gori M, Marinai S, Sheng J, Soda G, Sperduti A (1998) Logo recognition by recursive neural networks. In: Graphics Recognition Algorithms and Systems. Springer, pp 104–117

  60. Gelernter J (2009) Image indexing in article component databases. J Amer Soc Inf Sci Technol 60(10):1965–1976

    Google Scholar 

  61. GREC (2003) International symbol recognition contest at grec2003

  62. Groen FC, Sanderson AC, Schlag JF (1985) Symbol recognition in electrical diagrams using probabilistic graph matching. Pattern Recogn Lett 3(5):343–350

    Google Scholar 

  63. Hamada AH (1991) Structural recognition of disturbed symbols using discrete relaxation. Centre de Recherche en Informatique

  64. Hilaire X, Tombre K (2006) Robust and accurate vectorization of line drawings. IEEE Trans Pattern Anal Mach Intell 28(6):890–904

    Google Scholar 

  65. Hornby AS, Wehmeier S (1995) Oxford advanced learner’s dictionary, vol 1428. Oxford University Press, Oxford

    Google Scholar 

  66. Hoshino T, Suzuki S, Kosugi M (1986) Automatic input method for large scale maps. In: Proceedings of the 8th International Conference on Pattern Recognition, pp 449–453

  67. ISO I (2007) Standard graphical symbols: Public information symbols (iso 7001: 2007). International Standards Organisation (ISO), Geneva

    Google Scholar 

  68. Jain AK, Duin RP, Mao J (2000) Statistical pattern recognition: A review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Google Scholar 

  69. Janssen RD, Vossepoel AM (1997) Adaptive vectorization of line drawing images. Comput Vis Image Understand 65(1):38–56

    Google Scholar 

  70. Jiang X, Münger A, Bunke H (2000) Synthesis of representative graphical symbols by computing generalized median graph. In: Graphics Recognition Recent Advances. Springer, pp 183–192

  71. Jouili S, Tabbone S (2011) Towards performance evaluation of graph-based representation. In: Graph-Based Representations in Pattern Recognition. Springer, pp 72–81

  72. Kadyrov A, Petrou M (2001) The trace transform and its applications. IEEE Trans Pattern Anal Mach Intell 23(8):811–828

    Google Scholar 

  73. Kamila N, Mahapatra S, Nanda S (2005) Retracted: Invariance image analysis using modified zernike moments. Pattern Recogn Lett 26(6):747–753

    Google Scholar 

  74. Kanungo T, Haralick R, Dori D (1995) Understanding engineering drawings: A survey. In: Proceedings of First IARP Workshop on Graphics Recognition, pp 217–228

  75. Kassim AA, Tan T, Tan K (1999) A comparative study of efficient generalised hough transform techniques. Image Vis Comput 17(10):737–748

    Google Scholar 

  76. Kasturi R, Bow ST, El-Masri W, Shah J, Gattiker JR, Mokate UB (1990) A system for interpretation of line drawings. IEEE Trans Pattern Anal Mach Intell 12(10):978–992

    Google Scholar 

  77. Kauppinen H., Seppänen T, Pietikäinen M (1995) An experimental comparison of autoregressive and fourier-based descriptors in 2d shape classification. IEEE Trans Pattern Anal Mach Intell 17(2):201–207

    Google Scholar 

  78. Kherfi ML, Ziou D, Bernardi A (2004) Image retrieval from the world wide web, Issues, techniques, and systems. ACM Comput Surv (CSUR) 36(1):35–67

    Google Scholar 

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

    Google Scholar 

  80. Kim S, Suh J, Kim J (1993) Recognition of logic diagrams by identifying loops and rectilinear polylines. In: 1993., Proceedings of the Second International Conference on Document Analysis and Recognition. IEEE, pp 349–352

  81. King S (1982) Syntactic pattern recognition and applications

  82. Kiyko VM (1995) Recognition of objects in images of paper based line drawings. In: 1995., Proceedings of the Third International Conference on Document Analysis and Recognition. IEEE, vol 2, pp 970–973

  83. Kultanen P, Oja E, Xu L (1990) {R} andomized \(\{\textit {H}\}\) ough \(\{\textit {T}\}\) ransform \(\{\)(RHT)} in engineering drawing vectorization system

  84. Kuner P, Ueberreiter B (1988) Pattern recognition by graph matching-combinatorial versus continuous optimization. Int J Pattern Recognit Artif Intell 2(03):527–542

    Google Scholar 

  85. Lam L, Lee S-W, Suen CY (1992) Thinning methodologies-a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 14(9):869–885

    Google Scholar 

  86. Lavirotte S, Pottier L (1997) Optical formula recognition. In: 1997., Proceedings of the Fourth International Conference on Document Analysis and Recognition. IEEE, vol 1, pp 357–361

  87. Lee S-W, Kim JH, Groen FC (1990) Translation-, rotation-and scale-invariant recognition of hand-drawn symbols in schematic diagrams. Int J Pattern Recognit Artif Intell 4(01):1–25

    Google Scholar 

  88. Lee SW (1992) Recognizing hand-drawn electrical circuit symbols with attributed graph matching. In: Structured document image analysis. Springer, pp 340–358

  89. Lee H-J, Lee M-C (1994) Understanding mathematical expressions using procedure-oriented transformation. Pattern Recogn 27(3):447–457

    Google Scholar 

  90. Lee W, Kara LB, Stahovich TF (2007) An efficient graph-based recognizer for hand-drawn symbols. Comput Graph 31(4):554–567

    Google Scholar 

  91. Lee H-L, Gong S-J, Chen L-H (2016) An online handwritten recognition system of music score. In: 2016 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, vol 2, pp 552–557

  92. Liao SX, Pawlak M (1998) On the accuracy of zernike moments for image analysis. IEEE Trans Pattern Anal Mach Intell 20(12):1358–1364

    Google Scholar 

  93. Lin C-S, Hwang C-L (1987) New forms of shape invariants from elliptic fourier descriptors. Pattern Recogn 20(5):535–545

    Google Scholar 

  94. Lin Y, Wenyin L, Jiang C (2004) A structural approach to recognizing incomplete graphic objects. In: 2004. ICPR 2004. Proceedings of the 17th International Conference on Pattern Recognition. IEEE, vol 1, pp 371–375

  95. Lin X, Gao L, Tang Z, Baker J, Sorge V (2014) Mathematical formula identification and performance evaluation in pdf documents. Int J Doc Anal Recogn (IJDAR) 17(3):239–255

    Google Scholar 

  96. Liu X (2012) Note symbol recognition for music scores. In: Intelligent Information and Database Systems. Springer, pp 263–273

  97. Lladós J (1997) Combining graph matching and hough transform for handdrawn graphical document analysis, Application to Architectural Drawings. Phd, Universitat Autonoma de Barcelona

  98. Lladós J, López-Krahe J, Martí E (1997) A system to understand hand-drawn floor plans using subgraph isomorphism and hough transform. Mach Vis Appl 10(3):150–158

    Google Scholar 

  99. Lladós J, Martí E, López-Krahe J (1999) A hough-based method for hatched pattern detection in maps and diagrams. In: 1999. ICDAR’99. Proceedings of the Fifth International Conference on Document Analysis and Recognition. IEEE, pp 479–482

  100. Lladós J, Sánchez G, Martí E (1998) A string based method to recognize symbols and structural textures in architectural plans. In: Graphics Recognition Algorithms and Systems. Springer, pp 91–103

  101. Lladós J, Valveny E, Sánchez G, Martí E (2002) Symbol recognition: Current advances and perspectives. In: Graphics Recognition Algorithms and Applications. Springer, pp 104–128

  102. Llados J, Sanchez G (2004) Graph matching versus graph parsing in graphics recognition a combined approach. Int J Pattern Recognit Artif Intell 18(03):455–473

    Google Scholar 

  103. Luqman MM, Brouard T, Ramel J-Y (2009) Graphic symbol recognition using graph based signature and bayesian network classifier. In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference on. IEEE, pp 1325–1329

  104. Luqman MM, Delalandre M, Brouard T, Ramel J-Y, Lladós J (2010) Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition, arXiv:1004.5427

  105. Luqman MM, Delalandre M, Brouard T, Ramel J-Y, Lladós J (2010) Fuzzy intervals for designing structural signature: An application to graphic symbol recognition. In: graphics recognition. Achievements, Challenges, and Evolution. Springer, pp 12–24

  106. Luqman MM, Lladós J, Ramel J-Y, Brouard T (2010) A fuzzy-interval based approach for explicit graph embedding. In: Recognizing patterns in signals, speech, images and videos. Springer, pp 93–98

  107. Luqman MM, Lladós J, Ramel J-Y, Brouard T (2011) Dimensionality reduction for fuzzy-interval based explicit graph embedding. In: Ninth IAPR International Workshop on Graphics RECognition, vol 9, pp 117–120

  108. Luqman MM, Ramel J-Y, Lladós J, Brouard T (2013) Fuzzy multilevel graph embedding. Pattern Recogn 46(2):551–565

    MATH  Google Scholar 

  109. Lutz S (2014) Whats right with a syntactic approach to theories and models. Erkenntnis 79(8):1475–1492

    MathSciNet  MATH  Google Scholar 

  110. Madej D (1991) An intelligent map-to-cad conversion system. In: Proceedings of 1st. Int. Conf. on Document Analysis and Recognition, pp 602–610

  111. Maes M (1991) Polygonal shape recognition using string-matching techniques. Pattern Recogn 24(5):433–440

    Google Scholar 

  112. Mehlhorn K, Brauer W, Rozenberg G, Salomaa A (1984) Data structures and algorithms. Volume 2: Graph algorithms and NP-completeness. Springer, Berlin

    Google Scholar 

  113. Messmer BT, Bunke H (2000) Efficient subgraph isomorphism detection: a decomposition approach. IEEE Trans Knowl Data Eng 12(2):307–323

    Google Scholar 

  114. Miao Q, Xu P, Li X, Song J, Li W, Yang Y (2017) The recognition of the point symbols in the scanned topographic maps. IEEE Trans Image Process 26 (6):2751–2766

    MathSciNet  MATH  Google Scholar 

  115. Min W, Tang Z, Tang L (1993) Using web grammar to recognize dimensions in engineering drawings. Pattern Recogn 26(9):1407–1416

    Google Scholar 

  116. MIYAO H, NAKANO Y (1996) Note symbol extraction for printed piano scores using neural networks*. IEICE Trans Inf Syst 79(5):548–554

    Google Scholar 

  117. Mouchère H, Zanibbi R, Garain U, Viard-Gaudin C (2016) Advancing the state of the art for handwritten math recognition: the crohme competitions, 2011–2014. Int J Doc Anal Recogn (IJDAR) 19(2):173–189

    Google Scholar 

  118. Myers GK, Mulgaonkar PG, Chen C-H, DeCurtins JL, Chen E (1996) Verification-based approach for automated text and feature extraction from raster-scanned maps. In: Graphics Recognition Methods and Applications. Springer, pp 190–203

  119. Nagy G (1968) State of the art in pattern recognition. IEEE 56(5):836–863

    Google Scholar 

  120. O’Gorman L (1995) Basic techniques and symbol-level recognition-an overview. In: Graphics Recognition Methods and Applications. Springer, pp 1–12

  121. Okazaki A, Kondo T, Mori K, Tsunekawa S, Kawamoto E (1988) An automatic circuit diagram reader with loop-structure-based symbol recognition. IEEE Trans Pattern Anal Mach Intell 10(3):331–341

    Google Scholar 

  122. Paek S, Smith JR (1998) Detecting image purpose in world wide web documents. In: Photonics West’98 Electronic Imaging. International Society for Optics and Photonics, pp 151–158

  123. Pal SK, Rosenfeld A (1991) A fuzzy medial axis transformation based on fuzzy disks. Pattern Recogn Lett 12(10):585–590

    Google Scholar 

  124. Park BG, Lee KM, Lee SU, Lee JH (2003) Recognition of partially occluded objects using probabilistic arg (attributed relational graph)-based matching. Comput Vis Image Underst 90(3):217–241

    MATH  Google Scholar 

  125. Patare MD, Joshi MS (2016) Hand-drawn digital logic circuit component recognition using svm. International Journal of Computer Applications 143(3):24–28

    Google Scholar 

  126. Pavlidis T (1977) Structural pattern recognition, vol 2, Springer-verlag, New York

  127. Persoon E, Fu K-S (1977) Shape discrimination using fourier descriptors. IEEE Trans Syst Man Cybern 7(3):170–179

    MathSciNet  Google Scholar 

  128. Pinjarkar L, Sharma M, Selot S (2018) Efficient system for color logo recognition based on self-organizing map and relevance feedback technique. In: Smart Computing and Informatics. Springer, pp 53–62

  129. Prokop RJ, Reeves AP (1992) A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP: Graph Models Image Process 54(5):438–460

    Google Scholar 

  130. Qureshi RJ, Ramel J-Y, Cardot H, Mukherji P (2007) Combination of symbolic and statistical features for symbols recognition. In: 2007. ICSCN’07. International Conference on Signal Processing, Communications and Networking. IEEE, pp 477–482

  131. Rabbani M, Khoshkangini R, Nagendraswamy H, Conti M (2016) Hand drawn optical circuit recognition. Procedia Comput Sci 84:41–48

    Google Scholar 

  132. Ramel J-Y, Boissier G, Emptoz H (2000) A structural representation adapted to handwritten symbol recognition. In: Graphics Recognition Recent Advances. Springer, pp 228–237

  133. Ramel J-Y, Vincent N, Emptoz H (2000) A structural representation for understanding line-drawing images. Int J Doc Anal Recognit 3(2):58–66

    Google Scholar 

  134. Randriamahefa R, Cocquerez JP, Fluhr C, Pepin F, Philipp S (1993) Printed music recognition. In: 1993., Proceedings of the Second International Conference on Document Analysis and Recognition. IEEE, pp 898–901

  135. Rebelo A, Capela G, Cardoso JS (2010) Optical recognition of music symbols. Int J Doc Anal Recogn (IJDAR) 13(1):19–31

    Google Scholar 

  136. Reiher E, Li Y, Donne VD, Lalonde M, Hayne C, Zhu C (1996) A system for efficient and robust map symbol recognition. In: 1996., Proceedings of the 13th International Conference on Pattern Recognition. IEEE, vol 3, pp 783–787

  137. Robles-Kelly A, Hancock ER (2004) String edit distance, random walks and graph matching. Int J Pattern Recognit Artif Intell 18(03):315–327

    MATH  Google Scholar 

  138. Rosenfeld A (1984) Image analysis: problems, progress and prospects. Pattern Recogn 17(1):3–12

    Google Scholar 

  139. Rosenfeld A (1986) Axial representations of shape. Comput Vis Graph Image Process 33(2):156–173

    MATH  Google Scholar 

  140. Rosenfeld A (1990) Array, tree and graph grammars. Syntactic and Structural Pattern Recognition: Theory and Applications, pp 85–115

  141. Rosenfeld A, Kak AC (2014) Digital picture processing, vol 1. Elsevier, New York

    MATH  Google Scholar 

  142. Rosin PL (1997) Techniques for assessing polygonal approximations of curves. IEEE Trans Pattern Anal Mach Intell 19(6):659–666

    Google Scholar 

  143. Rui Y, She AC, Huang TS (1997) A modified fourier descriptor for shape matching in mars. Ser Softw Eng Knowl Eng 8:165–180

    Google Scholar 

  144. Rusiñol M., Lladós J. (2006) Symbol spotting in technical drawings using vectorial signatures. In: Graphics Recognition. Ten Years Review and Future Perspectives. Springer, pp 35–46

  145. Rusiñol M, Lladós J (2008) Word and symbol spotting using spatial organization of local descriptors. In: 2008. DAS’08. The Eighth IAPR International Workshop on Document Analysis Systems. IEEE, pp 489–496

  146. Rusinol M, Llados J (2009) Logo spotting by a bag-of-words approach for document categorization. In: 2009. ICDAR’09. 10th International Conference on Document Analysis and Recognition. IEEE, pp 111–115

  147. Sahoo PK, Soltani S, Wong AK (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41(2):233–260

    Google Scholar 

  148. Samet H, Soffer A (1996) Marco: Map retrieval by content. IEEE Trans Pattern Anal Mach Intell 18(8):783–798

    Google Scholar 

  149. Sánchez G, Lladós J (2001) A graph grammar to recognize textured symbols. In: 2001. Proceedings. Sixth International Conference on Document Analysis and Recognition. IEEE, pp 465–469

  150. Santosh K, Lamiroy B, Ropers J-P (2009) Inductive logic programming for symbol recognition. In: 2009. ICDAR’09. 10th International Conference on Document Analysis and Recognition. IEEE, pp 1330–1334

  151. Santosh K, Lamiroy B, Wendling L (2014) Integrating vocabulary clustering with spatial relations for symbol recognition. Int J Doc Anal Recogn (IJDAR) 17 (1):61–78

    Google Scholar 

  152. Santosh K, Wendling L, Lamiroy B (2014) Bor: Bag-of-relations for symbol retrieval, vol 28

  153. Seo W, Koo HI, Cho NI (2015) Junction-based table detection in camera-captured document images. Int J Doc Anal Recogn (IJDAR) 18(1):47–57

    Google Scholar 

  154. Soffer A, Samet H (1998) Using negative shape features for logo similarity matching. In: 1998. Proceedings. Fourteenth International Conference on Pattern Recognition. IEEE, vol 1, pp 571–573

  155. Stückelberg MV, Doermann D (1999) On musical score recognition using probabilistic reasoning. In: 1999. ICDAR’99. Proceedings of the Fifth International Conference on Document Analysis and Recognition. IEEE, pp 115–118

  156. Suzuki S, Yamada T (1990) Maris: Map recognition input system. In: Mapping and spatial modelling for navigation. Springer, pp 95–116

  157. Tabbone S, Wendling L (2003) Binary shape normalization using the radon transform. In: Discrete Geometry for Computer Imagery. Springer, pp 184–193

  158. Tabbone S, Wendling L, Tombre K (2003) Matching of graphical symbols in line-drawing images using angular signature information. Doc Anal Recogn 6(2):115–125

    Google Scholar 

  159. Tabbone S, Wendling L, Salmon J-P (2006) A new shape descriptor defined on the radon transform. Comput Vis Image Underst 102(1):42–51

    Google Scholar 

  160. Tan Q, Mitra P, Giles CL (2009) Effectively searching maps in web documents. In: Advances in Information Retrieval. Springer, pp 162–176

  161. Tang YY, Lee S-W, Suen CY (1996) Automatic document processing: a survey. Pattern Recogn 29(12):1931–1952

    Google Scholar 

  162. Teague MR (1980) Image analysis via the general theory of moments*. JOSA 70(8):920–930

    MathSciNet  Google Scholar 

  163. Teh C-H, Chin RT (1988) On image analysis by the methods of moments. IEEE Trans Pattern Anal Mach Intell 10(4):496–513

    MATH  Google Scholar 

  164. Tombre K (1997) Analysis of engineering drawings: State of the art and challenges. In: Graphics Recognition Algorithms and Systems. Springer, pp 257–264

  165. Tombre K, Ah-Soon C, Dosch P, Habed A, Masini G (1998) Stable, robust and off-the-shelf methods for graphics recognition, vol 1, IEEE

  166. Valveny E, Marti E (2000) Hand-drawn symbol recognition in graphic documents using deformable template matching and a bayesian framework. In: 2000. Proceedings. 15th International Conference on Pattern Recognition. IEEE, vol 2, pp 239–242

  167. Viswanathan M (1992) Analysis of scanned documents a syntactic approach. In: Structured Document Image Analysis. Springer, pp 115–136

  168. Vo Q, Lee G, Kim S, Yang H (2017) Recognition of music scores with non-linear distortions in mobile devices. Multimedia Tools and Applications:1–19. https://doi.org/10.1007/s1104

  169. Wall K, Danielsson P-E (1984) A fast sequential method for polygonal approximation of digitized curves. Graph Image Process Comput Vis 28(2):220–227

    Google Scholar 

  170. Wenyin L, Qian W, Xiao R, Jin X (2001) Smart sketchpad-an on-line graphics recognition system. In: 2001. Proceedings. Sixth International Conference on Document Analysis and Recognition. IEEE, pp 1050–1054

  171. Wenyin L, Zhang W, Yan L (2007) An interactive example-driven approach to graphics recognition in engineering drawings. Int J Doc Anal Recogn (IJDAR) 9 (1):13–29

    Google Scholar 

  172. Xiaogang X, Zhengxing S, Binbin P, Xiangyu J, Wenyin L (2004) An online composite graphics recognition approach based on matching of spatial relation graphs. Doc Anal Recogn 7(1):44–55

    Google Scholar 

  173. Yadid-Pecht O, Gerner M, Dvir L, Brutman E, Shimony U (1996) Recognition of handwritten musical notes by a modified neocognitron. Mach Vis Appl 9(2):65–72

    Google Scholar 

  174. Yajie Y, Zhang W, Wenyin L (2007) A new syntactic approach to graphic symbol recognition. In: 2007. ICDAR 2007. Ninth International Conference on Document Analysis and Recognition. IEEE, vol 1, pp 516–520

  175. Yang D, Webster JL, Renmdell L, Garrett Jr JH, Shaw DS (1993) Management of graphical symbols in a cad environment: a neural network approach. In: 1993. TAI’93. Proceedings., Fifth International Conference on Tools with Artificial Intelligence. IEEE, pp 272–279

  176. Yang S (2005) Symbol recognition via statistical integration of pixel-level constraint histograms: a new descriptor. IEEE Trans Pattern Anal Machi Intell 2:278–281

    Google Scholar 

  177. Zahn CT, Roskies RZ (1972) Fourier descriptors for plane closed curves. IEEE Trans Comput 100(3):269–281

    MathSciNet  MATH  Google Scholar 

  178. Zhang D, Lu G (2002) Shape-based image retrieval using generic fourier descriptor. Signal Process Image Commun 17(10):825–848

    Google Scholar 

  179. Zhang D, Lu G (2005) Study and evaluation of different fourier methods for image retrieval. Image Vis Comput 23(1):33–49

    Google Scholar 

  180. Zhang W, Wenyin L, Zhang K (2006) Symbol recognition with kernel density matching. IEEE Trans Pattern Anal Mach Intell 12:2020–2024

    Google Scholar 

  181. Zhang J, Du J, Dai L (2018) Multi-scale attention with dense encoder for handwritten mathematical expression recognition, arXiv:1801.03530

  182. Zhu G, Doermann D (2009) Logo matching for document image retrieval. In: 2009. ICDAR’09. 10th International Conference on Document Analysis and Recognition. IEEE, pp 606–610

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Authors and Affiliations

  1. Sarhad University of Science and Information Technology, Peshawar, Pakistan

    Irshad Khan & Murad Khan

  2. Islamia College University, Peshawar, Pakistan

    Naveed Islam

  3. FAST-National University of Computer and Emerging Sciences, Peshawar, Pakistan

    Hafeez Ur Rehman

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  1. Irshad Khan
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  2. Naveed Islam
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  3. Hafeez Ur Rehman
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  4. Murad Khan
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Khan, I., Islam, N., Ur Rehman, H. et al. A comparative study of graphic symbol recognition methods. Multimed Tools Appl 79, 8695–8725 (2020). https://doi.org/10.1007/s11042-018-6289-6

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