Character Recognition Experiments Using Unipen Data

Marc Parizeau, Alexandre Lemieux, Christian Gagné

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.