{"title":"Alphanumeric Hand-Prints Classification: Similarity Analysis between Local Decisions","authors":"G. Dimauro, S. Impedovo, M.G. Lucchese, R. Modugno, G. Pirlo","volume":6,"journal":"International Journal of Computer and Information Engineering","pagesStart":1852,"pagesEnd":1856,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/12774","abstract":"<p>This paper presents the analysis of similarity between local decisions, in the process of alphanumeric hand-prints classification. From the analysis of local characteristics of handprinted numerals and characters, extracted by a zoning method, the set of classification decisions is obtained and the similarity among them is investigated. For this purpose the Similarity Index is used, which is an estimator of similarity between classifiers, based on the analysis of agreements between their decisions. The experimental tests, carried out using numerals and characters from the CEDAR and ETL database, respectively, show to what extent different parts of the patterns provide similar classification decisions.<\/p>\r\n","references":"[1] C.Y. Suen, J. Guo, Z.C. Li, \" Analysis and Recognition of Alphanumeric Handprints by parts\", IEEE T-SMC, 1994, Vol. 24(4). pp. 614-630.\r\n[2] S. Mori, C.Y. Suen, K. Yamamoto, \"Historical Review of OCR research and\r\ndevelopment\", Proc. IEEE, 1992, Vol. 80, pp. 1029-1058.\r\n[3] O.D. Trier, A.K. Jain, T.Taxt, \"Feature Extraction Methods For Character\r\nRecognition - A survey\", Pattern Recognition , 1996, Vol 29(4), pp. 641-662.\r\n[4] C.Y. Suen, C. Nadal, R. Legault, T.A.Mai, L. Lam, \"Computer Recognition\r\nof unconstrained handwritten numerals\", Proc. IEEE, 1992, Vol 80, pp.\r\n1162-1180.\r\n[5] G.Dimauro, S.Impedovo, G.Pirlo, \"Multiple Experts:A New Methodology\r\nfor the Evaluation of the Combination Processes\", IWFHR-5,\r\nColchester,Uk,1996,pp.131-136.\r\n[6] G. Baptista, K.M. Kulkarmi, \"A high accuracy algorithm for recognition of\r\nhand-written numerals\", Pattern Recognition , 1988, Vol. 4, pp. 287-291.\r\n[7] M. Bokser, Omnidocument Technologies, Proc. IEEE, 1992, Vol. 80, pp.\r\n1066-1078.\r\n[8] F. Kimura, M. Shridar, \"Handwritten Numerical Recognition Based on\r\nMultiple Algorithms\", Pattern Recognition , 1991, Vol. 24 (10), pp. 969-983.\r\n[9] http:\/\/www.cedar.buffalo.edu\/Databases\/\r\n[10] http:\/\/axiongw.ee.uec.ac.jp\/japanese\/link\/resources\/database\/ETLCDB.html\r\n[11] Heutte L., Paquet T., Moreau J. V., Lecourtier Y., Olivier C. A structural \/\r\nStatistical Features Based Vector for Handwritten Character Recognition.\r\nPattern Recognition Letters 1998, 9:629-641.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 6, 2007"}