MJCN: Multi-objective Jaya Convolutional Network for handwritten optical character recognition

Abstract

In recent years, the non-handcrafted feature extraction methods have gained increasing popularity for solving pattern classification tasks due to their inherent ability to extract robust features and handle outliers. However, the design of such features demands a large set of training data. Meta-heuristic optimization schemes can facilitate feature learning even with a small amount of training data. This paper presents a new feature learning mechanism called multi-objective Jaya convolutional network (MJCN) that attempts to learn meaningful features directly from the images. The proposed scheme, unlike the convolutional neural networks, comprises a convolution layer, a multiplication layer, an activation layer and an optimizer known as multi-objective Jaya optimizer (MJO). The convolution layer searches meaningful patterns in an image through the local neighborhood connections and the multiplication layer projects the convolutional response to a more compact feature space. The weights used in these layers are initialized randomly and MJO is then introduced to optimize the weights. The main objective of MJO is to maximize the inter-class distance and minimize the intra-class variance. The feature vectors are finally derived using the optimized weights. The derived features are finally fed to a set of standard classifiers for recognition of characters. The performance of the proposed model is evaluated on various benchmark datasets, namely, NITR Odia handwritten character, ISI Kolkata Odia numeral, ISI Kolkata Bangla numeral, and MNIST as well as a newly developed dataset NITR Bangla numeral. The experimental results show that the proposed scheme outperforms other state-of-the-art approaches in terms of recognition accuracy.