Abstract
Background: The data proliferation has been resulted in large-scale, high dimensional data and brings new challenges for feature selection in handwriting recognition problems. The practical challenges like the large variability and ambiguities present in the individual’s handwriting style demand an optimal feature selection algorithm that would be capable to enhance the recognition accuracy of handwriting recognition system with reduced training efforts and computational cost.
Objective: This paper gives emphasis on the feature selection process and proposed a genetic algorithm based feature selection technique for handwritten digit recognition.
Methods: A hybrid feature set of statistical and geometrical features is developed in order to get the effective feature set consist of local and global characteristics of sample digits. The method utilizes a genetic algorithm based feature selection for selecting best distinguishable features and k-nearest neighbour for evaluating the fitness of features of handwritten digit dataset.
Results: The experiments are carried out on standard The Chars74K handwritten digit dataset and reported a 66% reduction in the original feature set without sacrificing the recognition accuracy.
Conclusion: The experiment results show the effectiveness of the proposed approach.
Keywords: Feature reduction, neural networks, feature selection, genetic algorithm, digit recognition, recognition problem.
Graphical Abstract
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