Character recognition experiments using Unipen data

Author

Parizeau, Marc ; Lemieux, Alexandre ; Gagne, Christian

Author_Institution

Dept. de Genie Electr. et de Genie Inf., Laval Univ., Ste-Foy, Que., Canada

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

481

Lastpage

485

Abstract

This paper presents experiments that compare the performances of several versions of a regional-fuzzy representation (RFR) developed for cursive handwriting recognition (CHR). These experiments are conducted using a common neural network classifier namely a multilayer perceptron (MLP) trained with backpropagation. Results are given for isolated digits, isolated lower-case letters and lower-case letters extracted from phrases, from the Unipen database. Data set Train-R01/V07 is used for training while DevTest-R01/V02 is used for testing. The best overall representation yields recognition rates of respectively 97.0% and 85.6% for isolated digits and lower case, and 84.4% for lower-case extracted from phrases

Keywords

backpropagation; fuzzy set theory; handwritten character recognition; multilayer perceptrons; pattern classification; CHR; DevTest-R01/V02; MLP; RFR; Train-R01/V07; Unipen data; backpropagation; character recognition experiments; cursive handwriting recognition; isolated digits; isolated lower-case letters; multilayer perceptron; neural network classifier; regional-fuzzy representation; Backpropagation; Character recognition; Data mining; Feature extraction; Handwriting recognition; Neural networks; Pattern classification; Pattern recognition; Spatial databases; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7695-1263-1

Type

conf

DOI

10.1109/ICDAR.2001.953836

Filename

953836