• DocumentCode
    567422
  • Title

    An algorithm development for handwritten character recognition by personal handwriting identity analysis [PHIA]

  • Author

    Boribalburephan, Perut ; Sakboonyarat, Boonnatee

  • Author_Institution
    Comput. & Technol. Dept., Mahidol Wittayanusorn Sch., Nakhon Pathom, Thailand
  • fYear
    2012
  • fDate
    7-8 July 2012
  • Firstpage
    6
  • Lastpage
    10
  • Abstract
    The algorithm for online handwritten character recognition, PHIA algorithm, is introduced. The algorithm uses a likelihood score computed by a small neural network from every symbol pair for various decisions. Scores are used to generate a relationship map (Rivals/Non-rivals) between each symbol pairs. The training data is added to the database if and only if the relationship with the training data is `rival´ for all existing database samples that identifies the same symbol. In the recognition phase, a nearest neighbor search is applied. During the search, if we traverse to a node whose relationship to the input is `non-rival´, we later skip all processes that would operate on that node´s rivals. This optimizes the decision path for each of the individual and enhances the ability to learn new symbols effectively.
  • Keywords
    handwriting recognition; handwritten character recognition; learning (artificial intelligence); neural nets; search problems; PHIA; decision path; likelihood score; nearest neighbor search; neural network; node rival; online handwritten character recognition; personal handwriting identity analysis; relationship map; symbol identification; symbol learning; symbol pair; Accuracy; Classification algorithms; Doping profiles; Geometry; Histograms; Training; Analytic Geometry; Artificial Neural Network (ANN); Greedy Search; K-Nearest Neighbor Search (NNS);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge and Smart Technology (KST), 2012 4th International Conference on
  • Conference_Location
    Chonburi
  • Print_ISBN
    978-1-4673-2166-2
  • Type

    conf

  • DOI
    10.1109/KST.2012.6287732
  • Filename
    6287732