Two-stage classification approach for the recognition of handwritten atomic Odia character

Abstract

This paper presents a two-stage classification mechanism for the identification of isolated (segmented) Odia basic handwritten character. Some preprocessing techniques have been applied for the removal of noise and image enhancement of each sample in the database. The database has been divided into two groups considering some essential characteristics of the shape of each character. A linear support vector machine (SVM) classifier is utilised for this purpose and results in an error rate of 0.0014%. In the second phase, the structural features extracted followed by PCA-based feature reduction technique to derive the discriminant features from each character in the groups for classification. In this phase, two three-layer back propagation neural networks (BPNN) are used to recognise handwritten Odia segmented character in each group. A relative investigation has been made on a sensibly generous dataset with the competent schemes. From test outcomes, we reason that our proposed method outmanoeuvres different schemes on OHCS v1.0 database and provides an overall accuracy of 98.87%.