Unconstrained handwriting recoganization basedon neural network using connectionist temporal classification token passing algorithm

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Recognition of human handwriting which offers the new way to improve the computer interface with the human and this process is very much useful for documents.Keyword spotting refers the spontaneous recognition of handwritten text, letter, and scripts from historical hand written books and the procedure of recovering all instance of a known keyword from an article. With a specific end goal to choose new components this paper, propose "a repetitive neural system manually written acknowledgment framework" for watchword spotting.

    The watchword seeing is finished utilizing an adjustment of the connectionist temporat classification Token Passing calculation in coincidence with a repetitive neural system. The proposed watchword spotting technique for written by hand message utilizing neural system, with another adaptation of connectionist temporat classification Token Pass calculation with quick and reliable catchphrase spotting can be executed without utilizing any content line or portioning separate words.

     


  • Keywords


    Connectionist Temporat Classification Token Passing Calculation; Keyword Spotting; Handwriting Acknowledgment; Intermittent Neural System (RNN).

  • References


      [1] Ashutosh Aggarwal, Rajneesh Rani, RenuDhir, Handwritten Devanagari Character Recognition Using Gradient Features, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 5, May 2012, 85-90.

      [2] Ms. GeetanjaliBhagwani, Ms. Ompriya Kale, ”Keyword Recognition by Improving Recurrent Neural Network using Character Model”, IJSRD - International Journal for Scientific Research & Development, Vol. 3, Issue 04, 2015 , ISSN (online): 2321-0613

      [3] A. Vinciarelli, “A Survey on Off-Line Cursive Word Recognition,” Pattern Recognition, vol. 35, no. 7, pp. 1433–1446, 2002.https://doi.org/10.1016/S0031-3203(01)00129-7.

      [4] R. Plamondon and S. N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63–84, 2000.https://doi.org/10.1109/34.824821.

      [5] Faisal Tehseen Shah, Kamran Yousaf,” Handwritten Digit Recognition Using Image Processing and Neural Networks” Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K.

      [6] R. Seiler, M. Schenkel, and F. Eggimann, “Off-Line Cursive Handwriting Recognition Compared with On-Line Recognition,” Proc. 13th Int’l Conf. Pattern Recognition, vol. 4, p. 505, 1996.

      [7] V. Frinken, A. Fórnes, A. Fischer, H.Bunke, Co-training forhandwritten word recognition, in: International Conference on Document Analysis and Recognition, 2011, pp.314–318.

      [8] Popescu, “A Line-Oriented Approach to Word Spotting in Handwritten Documents,” Pattern Analysis and Applications, vol. 3, pp. 153–168, 2000.https://doi.org/10.1007/s100440070020.

      [9] H. Cao, A. Bhardwaj, and V. Govindaraju, “A Probabilistic Methodfor Keyword Retrieval in Handwritten Document Images,” Pattern Recognition, vol. 42, no. 12, pp. 3374–3382, December 2009. [Online].Available: https://doi.org/10.1016/j.patcog.2009.02.003.

      [10] V. Frinken, H. Bunke, Self-Training for hand written text line recognition, in: 15thIberoamericanCongressonPatternRecognition, 2010, pp.104–112.

      [11] G. R. Ball, S. N. Srihari, Semi-supervisedlearningforhandwritingrecognition,in: 10thInternationalConferenceonDocumentAnalysisandRecognition,2009,pp.26–30.

      [12] R. Manmatha and T. M. Rath, “Indexing of Handwritten HistoricalDocuments - Recent Progress,” in Symposium on Document ImageUnderstanding Technology, 2003, pp. 77–85.

      [13] Y. Lu and C. L. Tan, “Word Spotting in Chinese Document Images withoutLayout Analysis,” in 16th Int’l Conference on Pattern Recognition,2002, pp. 57–60.

      [14] J. Rothfeder, S. Feng, and T. M. Rath, “Using Corner Feature Correspondencesto Rank Word Images by Similarity,” in Workshop on DocumentImage Analysis and Retrieval, 2003, p. 30.

      [15] D.Yu,B.Varadarajan,L.Deng,A.Acero,Activelearningandsemi-supervisedlearning forspeechrecognition:aunifiedframeworkusingtheglobalentropyreduction maximizationcriterion,ComputerSpeechandLanguage24(3) (2010)433–444.

      [16] K.Nigam,R.Ghani,Understandingthebehaviorofco-training,in:KDD-2000 WorkshoponTextMining,2000,pp.105–107.

      [17] P.Dreuw,D.Rybach,C.Gollan,H.Ney,Writeradaptivetrainingandwriting variantmodelrefinement foroffline Arabic handwritingrecognition,in:10th International ConferenceonDocumentAnalysisandRecognition,vol.1,2009, pp. 21–25.

      [18] S. Fern´andez, A. Graves, and J. Schmidhuber, “An Application ofRecurrent Neural Networks to Discriminative Keyword Spotting,” in17th Int’l Conf. on Artificial Neural Networks, ser. Lecture Notes inComputer Science, vol. 4669, 2007, pp. 220–229.

      [19] M. Wollmer, F. Eyben, J. Keshet, A. Graves, B. Schuller, and G. Rigoll,“Robust Discriminative Keyword Spotting for Emotionally ColoredSpontaneous Speech Using Bidirectional LSTM Networks,” in IEEEInt’l Conf. on Acustics, Speech and Signal Processing, 2009, pp. 3949–3952

      [20] Anita Pal & Dayashankar Singh, “Handwritten English Character Recognition Using Neural,” Network International Journal of Computer Science & Communication.vol. 1, No. 2, July-December 2010, pp. 141-144.

      [21] T. E. de Campos, B. R. Babu, and M. Varma. Character recognition in natural images. In Proceedings of the International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, February 2009

      [22] R. Plamondon and S. N. Srihari, “On-line and off- line handwritten characterrecognition: A comprehensive survey,”IEEE. Transactions on Pattern Analysis andMachine Intelligence, vol. 22, no. 1, pp. 63-84, 2000.

      [23] N. Arica and F. Yarman-Vural, “An Overview of Character Recognition Focused onOff-line Handwriting”, IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews, 2001, 31(2), pp. 216 - 233.https://doi.org/10.1109/5326.941845.

      [24] U. Bhattacharya, and B. B. Chaudhuri, “Handwritten numeral databases of Indianscripts and multistage recognition of mixed numerals,” IEEE Transaction on Patternanalysis and machine intelligence, vol.31, No.3, pp.444-457, 2009.https://doi.org/10.1109/TPAMI.2008.88.

      [25] U. Pal, T. Wakabayashi and F. Kimura, “Handwritten numeral recognition of sixpopular scripts,” Ninth International conference on Document Analysis and RecognitionICDAR 07, Vol.2, pp.749-753, 2007.

      [26] K.Vijayakumar·C,Arun, “Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC”, Cluster Computing DOI 10.1007/s10586-017-1176-x,Sept 2017

      [27] K.Vijayakumar·C,Arun, Analysis and selection of risk assessment frameworks for cloud based enterprise applications”, Biomedical Research, ISSN: 0976-1683 (Electronic), January 2017

      [28] K. Vijayakumar and C. Arun, “Continuous Security Assessment of Applications in Cloud Environment”, International Journal of Control Theory and Applications, ISSN: 0974-5645 volume No. 9(36), , Page No. 533-541, 2016.


 

View

Download

Article ID: 9825
 
DOI: 10.14419/ijet.v7i1.9.9825




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.