A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer

    Authors

    J. J. LaViola;R. Zeleznik

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Pattern Anal. Mach. Intell.

    Keywords

    handwriting recognition; AdaBoost; writer dependence; writer; independence; pairwise classification; real-time systems; ONLINE HANDWRITING RECOGNITION; CHARACTER-RECOGNITION; CLASSIFICATION; ART; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    We present a practical technique for using a writer-independent recognition engine to improve the accuracy and speed while reducing the training requirements of a writer-dependent symbol recognizer. Our writer-dependent recognizer uses a set of binary classifiers based on the AdaBoost learning algorithm, one for each possible pairwise symbol comparison. Each classifier consists of a set of weak learners, one of which is based on a writer-independent handwriting recognizer. During online recognition, we also use the n-best list of the writer-independent recognizer to prune the set of possible symbols and, thus, reduce the number of required binary classifications. In this paper, we describe the geometric and statistical features used in our recognizer and our all-pairs classification algorithm. We also present the results of experiments that quantify the effect incorporating a writer-independent recognition engine into a writer-dependent recognizer has on accuracy, speed, and user training time.

    Journal Title

    Ieee Transactions on Pattern Analysis and Machine Intelligence

    Volume

    29

    Issue/Number

    11

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    1917

    Last Page

    1926

    WOS Identifier

    WOS:000249343900004

    ISSN

    0162-8828

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