Alphanumeric Hand-Prints Classification: Similarity Analysis between Local Decisions

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.





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.
[2] S. Mori, C.Y. Suen, K. Yamamoto, "Historical Review of OCR research and
development", Proc. IEEE, 1992, Vol. 80, pp. 1029-1058.
[3] O.D. Trier, A.K. Jain, T.Taxt, "Feature Extraction Methods For Character
Recognition - A survey", Pattern Recognition , 1996, Vol 29(4), pp. 641-662.
[4] C.Y. Suen, C. Nadal, R. Legault, T.A.Mai, L. Lam, "Computer Recognition
of unconstrained handwritten numerals", Proc. IEEE, 1992, Vol 80, pp.
1162-1180.
[5] G.Dimauro, S.Impedovo, G.Pirlo, "Multiple Experts:A New Methodology
for the Evaluation of the Combination Processes", IWFHR-5,
Colchester,Uk,1996,pp.131-136.
[6] G. Baptista, K.M. Kulkarmi, "A high accuracy algorithm for recognition of
hand-written numerals", Pattern Recognition , 1988, Vol. 4, pp. 287-291.
[7] M. Bokser, Omnidocument Technologies, Proc. IEEE, 1992, Vol. 80, pp.
1066-1078.
[8] F. Kimura, M. Shridar, "Handwritten Numerical Recognition Based on
Multiple Algorithms", Pattern Recognition , 1991, Vol. 24 (10), pp. 969-983.
[9] http://www.cedar.buffalo.edu/Databases/
[10] http://axiongw.ee.uec.ac.jp/japanese/link/resources/database/ETLCDB.html
[11] Heutte L., Paquet T., Moreau J. V., Lecourtier Y., Olivier C. A structural /
Statistical Features Based Vector for Handwritten Character Recognition.
Pattern Recognition Letters 1998, 9:629-641.