Devanagri Handwritten Numeral Recognition using Feature Selection Approach
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TLDR
An improvement in recognition result is reported when decision combiner based committee is used along with class related feature selection approach for the recognition of Devanagri handwritten numerals.Abstract:
In this paper novel feature selection approach is used for the recognition of Devanagri handwritten numerals. The numeral images used for the experiments in the study are obtained from standard benchmarking data-set created by CVPR (ISI)Kolkata. The recognition algorithm consists of four basic steps; pre-processing, feature generation, feature subset selection and classification. Features are generated from the boundary of characters, utilizing the direction based histogram of segmented compartment of the character image. The feature selection algorithm is utilizing the concept of information theory and is based on maximum relevance minimum redundancy based objective function. The classification results are obtained for a single neural network based classifier as well as for the committee of Neural Network based classifiers. The paper reports an improvement in recognition result when decision combiner based committee is used along with class related feature selection approach.read more
Citations
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Convolutional Neural Network based Handwritten Bengali and Bengali-English Mixed Numeral Recognition
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Convolutional Neural Network Training incorporating Rotation-Based Generated Patterns and Handwritten Numeral Recognition of Major Indian Scripts
TL;DR: In this paper, convolutional neural network (CNN) based two different methods have been investigated for better recognition of Bangla and Devnagari handwritten numerals, both the methods use rotation-based generated patterns along with ordinary patterns to train CNN but in two different modes.
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References
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An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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The Mathematical Theory of Communication
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.
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Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
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