This paper presents the latest results of handwritten digit recognition on well-known image databases using the state-of-the-art feature extraction and classification techniques. The tested databases are CENPARMI, CEDAR, and MNIST. On the test dataset of each database, 56 recognition accuracies are given by combining 7 classifiers with 8 feature vectors. All the classifiers and feature vectors give high accuracies. Among the features, the chain-code feature and gradient feature show advantages, and the profile structure feature shows efficiency as a complementary feature. In comparison of classifiers, the support vector classifier with RBF kernel gives the highest accuracy but is extremely expensive in storage and computation. Among the non-SV classifiers, the polynomial classifier performs best, followed by a learning quadratic discriminant function classifier. The results are competitive compared to previous ones and they provide a baseline for evaluation of future works.


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    Title :

    Handwritten digit recognition using state-of-the-art techniques


    Contributors:
    Cheng-Lin Liu, (author) / Nakashima, K. (author) / Sako, H. (author) / Fujisawa, H. (author)


    Publication date :

    2002-01-01


    Size :

    398288 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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