Classification of handwritten Odia basic character using Stockwell transform

In this paper, we have proposed a scheme for the recognition of handwritten basic isolated Odia characters. Our method utilises chain code histogram (CCH) to split the overall dataset into two different groups. Preprocessing is carried out for noise removal and enhancement of the character images in each group. Discrete orthonormal S-transform (DOST) features are extracted followed by a PCA-based feature reduction scheme to derive discriminant features to be used by the ANN for classification. Comparative analysis has been performed on a reasonably large dataset with the competent schemes. From experimental results, we conclude that our proposed scheme outperforms other schemes on the dataset that we have designed and provides an overall accuracy of 98.55%.