Dimensionality reduction

Computational Science and Engineering

Context (archaeology)

Linguistics

Philosophy

Pure mathematics

Biology

Curse of dimensionality

Dimension (graph theory)

Selection (genetic algorithm)

Set (abstract data type)

Computer science

Paleontology

Natural language processing

Character recognition

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Feature vector

Intelligent character recognition

Feature selection

Feature (linguistics)

Artificial intelligence

Geometry

Bengali

Programming language

Intelligent word recognition

Word (group theory)

Feature extraction

Machine learning

Speech recognition

Neural Computing and Applications

Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset.

A GA based hierarchical feature selection approach for handwritten word recognition