Character recognition based on non-linear multi-projection profiles measure

Abstract

In this paper, we study a method for isolated handwritten or hand-printed character recognition using dynamic programming for matching the non-linear multiprojection profiles that are produced from the Radon transform. The idea is to use dynamic time warping (DTW) algorithm to match corresponding pairs of the Radon features for all possible projections. By using DTW, we can avoid compressing feature matrix into a single vector which may miss information. It can handle character images in different shapes and sizes that are usually happened in natural handwriting in addition to difficulties such as multi-class similarities, deformations and possible defects. Besides, a comprehensive study is made by taking a major set of state-ofthe-art shape descriptors over several character and numeral datasets from different scripts such as Roman, Devanagari, Oriya, Bangla and Japanese-Katakana including symbol. For all scripts, the method shows a generic behaviour by providing optimal recognition rates but, with high computational cost.

Keywords

character recognition / the Radon features / dynamic programming / shape descriptors

References

[1] Plamondon R, Srihari S N. On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22: 63―84

[2] Arica N, Yarman-Vural F. An overview of character recognition focused on off-line handwriting. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2001, 31(2): 216―233

[3] Suen C Y, Berthod M, Mori S. Automatic recognition of handprinted characters —the state of the art. Proceedings of the IEEE, 1980, 68(4): 469―487

[4] Schantz H F. The History of OCR. Manchester Center, VT: Recognition Technologies Users Association, 1982

[5] Davis R H, Lyall J. Recognition of handwritten characters — a review. Image and Vision Computing, 1986, 4: 208―218

[6] Govindan V, Shivaprasad A. Character recognition — a review. Pattern Recognition, 1990, 23(7): 671―683

[7] Dai R, Liu C, Xiao B. Chinese character recognition: history, status and prospects. Frontiers of Computer Science, 2007, 1(2): 126―136

[8] Trier D, Jain A K, Taxt T. Feature extraction methods for character recognition — a survey. Pattern Recognition, 1996, 29(4): 641―662

[9] Heutte L, Paquet T, Moreau J V, Lecourtier Y, Olivier C. A structural/ statistical feature based vector for handwritten character recognition. Pattern Recognition Letters, 1998, 19(7): 629―641

[10] Foggia P, Sansone C, Tortorella F, Vento M. Combining statistical and structural approaches for handwritten character description. Image and Vision Computing, 1999, 17(9): 701―711

[11] Jain A K, Duin R P W, Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2000, 22(1): 4―37

[12] Shinghal R, Suen C. A method for selecting constrained hand-printed character shapes for machine recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, 4(1): 74―78

[13] Mantas J, Heaton A. Handwritten character recognition by parallel labelling and shape analysis. Pattern Recognition Letters, 1983, 1: 465―468

[14] Campos T E, Babu B R, Varma M. Character recognition in natural images. In: Proceedings of the International Conference on Computer Vision Theory and Applications. 2009, 273―280

[15] Mahmoud S A. Arabic character recognition using fourier descriptors and character contour encoding. Pattern Recognition, 1994, 27(6): 815―824

[16] Granlund G H. Fourier preprocessing for hand print character recognition. IEEE Transactions on Computers, 1972, C-21(2): 195―201

[17] Lai M T, Suen C Y. Automatic recognition of characters by fourier descriptors and boundary line encodings. Pattern Recognition, 1981, 14(1-6): 383―393

[18] Rauber T, Steiger Garcao A. Shape description by unl fourier featuresan application to handwritten character recognition. In: Proceedings of the 11th IAPR International Conference on Pattern Recognition Methodology and Systems. 1992, II: 466―469

[19] Hopkins J, Andersen T L. A fourier-descriptor-based character recognition engine implemented under the gamera open-source documentprocessing framework. In: Proceedings of the International Conference on Document Recognition and Retrieval Conference. 2005, 111―118

[20] Bernier T, Landry J A. A new method for representing and matching shapes of natural objects. Pattern Recognition, 2003, 36(8): 1711―1723

[21] Kopf S, Haenselmann T, Effelsberg W. Enhancing curvature scale space features for robust shape classification. In: Proceedings of the IEEE International Conference on Multimedia and Expo. 2005, 478―481

[22] Khoddami M, Behrad A. Farsi and latin script identification using curvature scale space features. In: Proceedings of the Symposium on Neural Network Applications in Electrical Engineering. 2010, 213―217

[23] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509―522

[24] Tepper M, Acevedo D, Goussies N A, Jacobo J C, Mejail M. A decision step for shape context matching. In: Proceedings of the IEEE International Conference on Image Processing. 2009, 409―412

[25] Flusser J. On the independence of rotation moment invariants. Pattern Recognition, 2000, 33(9): 1405―1410

[26] Kim W Y, Kim Y S. A region-based shape descriptor using zernike moments. Signal Processing: Image Communication, 2000, 16(1-2): 95―102

[27] Zhang D, Lu G. Shape-based image retrieval using generic fourier descriptor. Signal Processing: Image Communication, 2002, 17: 825―848

[28] Frejlichowski D. Analysis of four polar shape descriptors properties in an exemplary application. In: Proceedings of the International conference on Computer Vision and graphics. 2010, 376―383

[29] Zhang D, Lu G. Review of shape representation and description techniques. Pattern Recognition, 2004, 37(1): 1―19

[30] Tabbone S, Wendling L, Salmon J P. A new shape descriptor defined on the radon transform. Computer Vision and Image Understanding, 2006, 102(1): 42―51

[31] Deans S R. Applications of the Radon Transform. New York: Wiley Interscience Publications, 1983

[32] Kruskall J B, Liberman M. The symmetric time warping algorithm: From continuous to discrete. In: Proceedings of Time Warps, String Edits and Macromolecules: the Theory and Practice of String Comparison. 1983, 125―161

[33] Keogh E J, Pazzani M J. Scaling up dynamic time warping to massive dataset. In: Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases. 1999, 1―11

[34] Coetzer J. Off-line Signature Verification. Dissertation for PhD Degree, University of Stellenbosch, 2005

[35] Jayadevan R, Kolhe S R, Patil P M. Dynamic time warping based static hand printed signature verification. Pattern Recognition Research, 2009, 4(1): 52―65

[36] Santosh K C, Lamiroy B, Wendling L. DTW for matching radon features: a pattern recognition and retrieval method. In: Proceedings of International Conference on Advances Concepts for Intelligent Vision Systems. 2011, 249―260

[37] Santosh K C. Character recognition based on DTW-radon. In: Proceedings of International Conference on Document Analysis and Recognition. 2011, 264―268

[38] Santosh K C, Lamiroy B, Wendling L. DTW-radon-based shape descriptor for pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence, 2013, 27(03): 1350008

[39] Alginahi Y. Preprocessing techniques in character recognition. Character Recognition, 2010, 1―20

[40] Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62―66

[41] Keogh E J. Exact indexing of dynamic time warping. In: Proceedings of the 28th International Conference on Very Large Data Bases. 2002, 406―417

[42] Bhowmik T K, Parui S K, Bhattacharya U, Shaw B. An HMM based recognition scheme for handwritten oriya numerals. In: Proceedings of the International Conference on Information Technology. 2006, 105―110

[43] Bhattacharya U, Chaudhuri B B. Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(3): 444―457

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