A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition

In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into seg ments of equal pixel counts where the centroid of the character is kept as the or igin. Three primitive contour features namely, distance (l), angle (θ), and arc-to-ch ord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languages—Bangla and English. In general, we observed that our proposed contour features outperform other schemes. Keywords—Contour Features, Handwritten Character, Neural Classifier, Numeral Recognition, OCR, Odia.