A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer
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Updated: Jul 11, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
Published on: June 25, 2019
Joseph J LaViola1, Robert C Zeleznik
1School of Electrical Engineering and Computer Science, University of Central Florida, Engineering 3--Harris Center, Orlando, FL 32816-2362, USA. jjl@cs.ucf.edu
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.
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