A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer

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 algor...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2007-11, Vol.29 (11), p.1917-1926
Main Authors: LaViola, Joseph J., Zeleznik, Robert C.
Format: Article
Language:English
Subjects:
AdaBoost
Algorithms
Application software
Applied sciences
Artificial Intelligence
Automatic Data Processing - methods
Biometry - methods
Classification algorithms
Classifications
Classifiers
Computer Graphics
Computer science
control theory
systems
Data preprocessing
Documentation
Engines
Exact sciences and technology
Handwriting
Handwriting recognition
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Information Storage and Retrieval - methods
Models, Statistical
Numerical Analysis, Computer-Assisted
On-line systems
pairwise classification
Pattern Recognition, Automated - methods
Reading
Real time systems
Recognition
Reproducibility of Results
Robustness
Runtime
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Subtraction Technique
Symbols
Training
User-Computer Interface
writer dependence
writer independence
Writing
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2007.1109