Classifying Isogenous Fields

Authors:
Sriharsha Veeramachaneni;Hiromichi Fujisawa;Cheng-Lin Liu;George Nagy
Affiliations:
-;-;-;-
Venue:
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Year:
2002

Citing 0
Cited 3

Effects of Classifier Structures and Training Regimes on Integrated Segmentation and Recognition of Handwritten Numeral Strings

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Style Consistent Classification of Isogenous Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

Classifiers that utilize style context in co-occurring patterns increase recognition accuracy. When patterns occur as long isogenous fields, this gain should increase unless negated by parameter estimation errors that increase with field length. We show that our method achieves higher accuracy with longer input fields because it can be trained accurately. We also present some ongoing work on simple heuristics to reduce computational complexity of the scheme.