Dynamic zoning selection for handwritten character recognition

/in , , /by
Dynamic zoning selection for handwritten character recognition

Hirabara, Luciane Y. and Aires, Simone B.K. and Freitas, Cinthia O.A. and Britto, Alceu S. and Sabourin, Robert

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011

Abstract : This paper presents a two-level based character recognition method in which a dynamically selection of the most promising zoning scheme for feature extraction allows us to obtain interesting results for character recognition. The first level consists of a conventional neural network and a look-up-table that is used to suggest the best zoning scheme for a given unknown character. The information provided by the first level drives the second level in the selection of the appropriate feature extraction method and the corresponding class-modular neural network. The experimental protocol has shown significant recognition rates for handwritten characters (from 80.82% to 88.13%). © 2011 Springer-Verlag.

You might also like
[Seminar] Structural Equation Modeling to latent causal representation learning for more trustable ML
Dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Information based Deep Clustering: An experimental study
Why template self-update should work in biometric authentication systems?
An engineering approach to forecast volatility of financial indices
Multiple classifier system for plant leaf recognition
Discretely-constrained deep network for weakly supervised segmentation
A machine learning approach to automate facial expressions from physical activity