A multilevel recognition of Meitei Mayek handwritten characters using fusion of features strategy

Handwritten character recognition (HCR) is a challenging task because of high intra-class dissimilarity and high inter-class similarity among the character images. Therefore, the features that are used to represent the character images should be such that they maximize the inter-class variations and minimize intra-class variations. In this paper, a multilevel approach for recognition of Meitei Mayek handwritten characters using fusion of features strategy is presented. The proposed methodology has two levels of recognition. The first level employs a simple CNN. A method to identify the structurally similar character classes based on the probability values of the softmax function is also proposed. The fused feature set is employed in the second-level recognition to distinguish between these character classes, thereby enhancing the discriminative power of the fused feature set by reducing the search space in the second level. The fused feature set is a combination of traditional handcrafted feature descriptors and deep features learned by CNN. The fused feature set is fed to an SVM for second-level recognition. The experimental results show a superior performance of the proposed methodology in identifying the structurally similar character classes. This achieves an overall recognition accuracy of 97.08%, which sets the benchmark on the concerned Meitei Mayek handwritten character dataset. The methodology also shows significant results on MNIST, DIDA and CArDIS datasets.