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We present a practical technique for using a writerindependent recognition engine to improve the accuracy and speed while reducing the training requirements of a writerdependent 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 writerindependent 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.
Handwriting recognition, AdaBoost, writer dependence, writer independence, pairwise classification, real-time systems
Joseph J. LaViola Jr., Robert C. Zeleznik, "A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 1917-1926, November 2007, doi:10.1109/TPAMI.2007.1109
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