{"title":"An algorithm development for handwritten character recognition by personal handwriting identity analysis [PHIA]","authors":"P. Boribalburephan, B. Sakboonyarat","doi":"10.1109/KST.2012.6287732","DOIUrl":null,"url":null,"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.","PeriodicalId":209504,"journal":{"name":"Knowledge and Smart Technology (KST)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge and Smart Technology (KST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KST.2012.6287732","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
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.