An Enhanced Harmony Search Method for Bangla Handwritten Character Recognition Using Region Sampling

TL;DR

This paper introduces an enhanced Harmony Search-based region selection method for Bangla handwritten character recognition, significantly reducing the number of regions needed and improving recognition accuracy, thereby decreasing computational cost.

Contribution

It proposes a novel Harmony Search-based technique for selecting informative image regions, improving recognition efficiency and accuracy in Bangla handwritten character recognition.

Findings

Achieved up to 43.75% reduction in regions needed

Improved recognition accuracy by up to 2.3%

Reduced feature extraction time and cost

Abstract

Identification of minimum number of local regions of a handwritten character image, containing well-defined discriminating features which are sufficient for a minimal but complete description of the character is a challenging task. A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here. The powerful framework of Harmony Search has been utilized to search the region space and detect only the most informative regions for correctly recognizing the handwritten character. The proposed method has been tested on handwritten samples of Bangla Basic, Compound and mixed (Basic and Compound characters) characters separately with SVM based classifier using a longest run based feature-set obtained from the image subregions formed by a CG based quad-tree partitioning approach. Applying this methodology on the above mentioned three types of datasets, respectively 43.75%, 12.5% and 37.5% gains have been achieved in terms of region reduction and 2.3%, 0.6% and 1.2% gains have been achieved in terms of recognition accuracy. The results show a sizeable reduction in the minimal number of descriptive regions as well a significant increase in recognition accuracy for all the datasets using the proposed technique. Thus the time and cost related to feature extraction is decreased without dampening the corresponding recognition accuracy.