An Algorithm for Japanese Character Recognition

Автор: Soumendu Das, Sreeparna Banerjee

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 1 vol.7, 2014 года.

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In this paper we propose a geometry- topology based algorithm for Japanese Hiragana character recognition. This algorithm is based on center of gravity identification and is size, translation and rotation invariant. In addition, to the center of gravity, topology based landmarks like conjunction points masking the intersection of closed loops and multiple strokes, as well as end points have been used to compute centers of gravity of these points located in the individual quadrants of the circles enclosing the characters. After initial pre-processing steps like notarization, resizing, cropping, noise removal, synchronization, the total number of conjunction points as well as the total number of end points are computed and stored. The character is then encircled and divided into four quadrants. The center of gravity (cog) of the entire character as well as the cogs of each of the four quadrants are computed and the Euclidean distances of the conjunction and end points in each of the quadrants with the cogs are computed and stored. Values of these quantities both for target and template images are computed and a match is made with the character having the minimum Euclidean distance. Average accuracy obtained is 94.1 %.

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Japanese Optical Character Recognition, geometry, topology, image processing

Короткий адрес: https://sciup.org/15013464

IDR: 15013464

Список литературы An Algorithm for Japanese Character Recognition

  • Indira B., Shalini M., Ramana Murthy M.V., Mahaboob Sharrief Shaik, Classification and Recognition of printed Hindi characters using Artificial Neural Networks, Int. J. Image, Graphics, Signal Processing, 4(6), 15-21 (2012).
  • Dewangan Shailendra Kumar, Real Time Recognition of Handwritten Devanagiri Signatures without segmentation using Artificial Neural Networks, Int. J. Image, Graphics, Signal Processing,, 5(4), 30-37 (2013).
  • John Jomy, Balakrishnan Kannan, Pramode K.V., A system for Offline Recognition of Handwritten characters in Malayam Script, nt. J. Image, Graphics, Signal Processing,, 5(4), 53-59 (2013).
  • Das S and Banerjee S (2014), Survey of Pattern Recognition Approaches in Japanese Character Recognition, International Journal of Computer Science and Information Technology, Vol. 5(1) 93-99.
  • D. Barnes and M. Manic, STRICR-FB a Novel SIze-Translation Rotation Invariant Character Recognition Method (2010), Proceed. 6th Human System Interaction Conference, Rzeszow, Poland, 163-168.
  • Miia Sainio, Kazuo Bingushi, Raymond Bertram; Vision Research (2007); "The role of interword spacing in reading Japanese: An eye movement study"; Volume: 47, Issue: 20, Pages: 2577-2586.
  • Neural Networks which consist of Simple Recurrent Networks for Character Recognition by Template Matching, (2008) Y. Shimozawa, Journal of the Information Processing Society 49(10), 3703-3714.
  • The post processing of Character Recognition by Genetic Algorithms, Y Shimozawa, S. Okoma, (1999), Journal of the Information Processing Society, 40(3) 1106-1116.
  • Off-line Hand Printed Character Recognition System using directional HMM based on features of connected pixels, H. Nishimura, M. Tsutsumi, M. Maruyama, H. Miyao and Y. Nakano (2002), Journal of the Information Processing Society 43(12) 4051-4058.
  • Maruyama, K.-I.; Maruyama, M.; Miyao, H.; Nakano, Y.; "Hand printed Hiragana recognition using support vector machines"; Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on Digital Object Identifier: 10.1109/IWFHR.2002.1030884; 2002, Pages: 55 – 60.
  • Proposal of Character Segmentation using Convergence of the Shortest Path, E. Tan aka (2011) Meeting on Image Recognition and Understanding Proceedings (2011) MIRU 2011, 331-336.
  • An Online Character Recognition Algorithm RAV (Parametrized Angle Variations) M. Lubumbashi, S, Masai, O. Minamoto, Y. Nagasaki, Y. Kommunizma and T. Maturation, (2000), Journal of the Information Processing Society, 41(9), 2536-2544.
  • A Handwriting Character Recognition System Using Adaptive Context Processing, N. Okayama, (1999) IPSJ Technical Report on Information and Media (IM) 85-90.
  • An Efficient Indexing Scheme for Image Storage and Recognition, IEEE Transactions on Industrial Electronics, M. Al Mohamed, (1999) 46(2).
  • S. H. Kim, "Performance Improvement Strategies on Template Matching for Large Set Character Recognition", Proc. 17th International Conference on Computer Processing of Oriental Languages, pp 250-253, Hongkong, April 1997.
  • C. H. Ting, H. J. Lee, Y. J. Thai, "Multi stage per-candidate selection handwritten Chinese character recognition systems", Pattern Recognition, vol. 27, no.8, pp.1093-1102, 1994."
  • H. Tallahassee and T.D. Griffin, "Recognition enhancement by linear tournament verification", Prof. 2nd ICDAR, Tsunami, Japan, 1993, pp.585-588.
  • D.H. Kim, Y.S. Twang, S.T. Park, E.J. Kim, S.H. Park and S.Y. Bang, "Handwritten Korean character image database PE92", Prof. 2nd ICDAR, Tsunami, Japan, 1993, pp.470-473.
  • J. J. Hull, Document image matching and retrieval with multiple distortion--invariant descriptors, Prof. IAPR Workshop on Document Analysis Systems, pp. 383-399 (1994).
  • Snead, M. Minos and K. locked, Document image retrieval system using character candidates generated by character recognition process, Proc. Second Int. Cong. Document Analysis and Recognition, pp. 541- 546 (1993).
  • N. Nina, K. Kagoshima and Y. Shimmer, Post processing for character recognition using keyword information, IAPR Workshop Machine Vision Applications, pp. 519-522 (1992).
  • D.A. Dahl, L. M. Norton and S. L. Taylor, Improving OCR accuracy with linguistic knowledge, Proc. Second Ann. Symp. Document Analysis and Information Retrieval, pp. 169-177 (1993).
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