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This paper presents an optimized Hill Climbing algorithm to select a subset of features for handwritten character recognition. The search is conducted taking into account a random mutation strategy and the initial relevance of each feature in the recognition process. The experiments have shown a reduction in the original number of features used in an MLP-based character recognizer from 132 to 77 features (reduction of 42%) without a significant loss in terms of recognition rates, which are 99% for 60,089 samples of digits, and 93% for 11,941 uppercase characters, both handwritten samples from the NIST SD19 database. The proposed method has shown to be an interesting strategy to implement a wrapper approach without the need of complex and expensive hardware architectures.

Feature Subset Selection Using an Optimized Hill Climbing Algorithm for Handwritten Character Recognition