Title

A Practical Approach For Writer-Dependent Symbol Recognition Using A Writer-Independent Symbol Recognizer

Keywords

AdaBoost; Handwriting recognition; Pairwise classification; Real-time systems; Writer dependence; Writer independence

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 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 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. © 2007 IEEE.

Publication Date

11-1-2007

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

29

Issue

11

Number of Pages

1917-1926

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPAMI.2007.1109

Socpus ID

34548803560 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34548803560

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