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Character Recognition Experiments using Unipen Data

Marc Parizeau, Alexandre Lemieux and 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 (NN) classifier, namely a Multi-Layer Perceptron (MLP) trained with backpropagation. Results are given for Sections 1a (isolated digits), 1c (isolated lower-case), and part of Section 3 (lower-case extracted from phrases) of 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.

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    author    = { Marc Parizeau and Alexandre Lemieux and Christian Gagné },
    title     = { Character Recognition Experiments using Unipen Data },
    booktitle = { Proc. of the International Conference on Document Analysis and Recognition (ICDAR) },
    pages     = { 481-485 },
    year      = { 2001 },
    month     = { September 10-13 },
    location  = { Seatle }

Dernière modification: 2002/06/14 par parizeau


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