Region selection in handwritten character recognition using Artificial Bee Colony Optimization

Detection of local regions with optimal discriminating information from a sample of handwritten character image is one of the most challenging tasks to the pattern recognition community. In order to identify such regions, the idea of Artificial Bee Colony Optimization has been utilized in the present work. The technique is evaluated to pin point the set of local regions offering optimal discriminating feature set for handwritten numeral and character recognition. Initially, 8 directional gradient features are extracted from every region of different levels of partitions created using a CG based Quad Tree partitioning approach. Then, using the present approach, at each level, sampling process is done based on support Vector Machine (SVM) in every single region. Applying the technique we have gained 33%, 14%, 9%, 19%interms of region reduction and 0.2%, 0.4%, 0%, 1.6% in terms of recognition for Arabic, Hindi, Telugu numerals and Bangla Basic character datasets respectively. Though the success rate has not improved significantly for all the datasets, sizable amount of reduction in regions has occurred for every dataset using the present technique. Thus the cost and time of feature extraction is reduced significantly without dropping the general recognition rate.