Memetic Algorithm Based Feature Selection for Handwritten City Name Recognition

Feature selection plays a key role to reduce the high-dimensionality of feature space in machine learning applications by discarding irrelevant and redundant features with the aim of obtaining a subset of features that accurately describe a given problem with a minimum or no degradation of performance. In this paper, a Memetic Algorithm (MA) based Wrapper-filter feature selection framework is proposed for the recognition of handwritten Bangla city names. For evaluating the MA framework, a recently published feature extraction technique, reported in [1], is used for the said pattern recognition problem. Experimentation is conducted on an in-house dataset of 6000 words written in Bangla script. Here, 40 most popular city names of West Bengal, a state in India, have been considered to prepare the dataset. Proposed technique not only reduces the feature dimension, but also enhances the performance of the word recognition technique significantly.

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