A Scale and Rotation Invariant Scheme for Multi-oriented Character Recognition

In printed stylized documents, text lines may be curved in shape and as a result characters of a single line may be multi-oriented. This paper presents a multi-scale and multi-oriented character recognition scheme using foreground as well as background information. Here each character is partitioned into multiple circular zones. For each zone, three centroids are computed by grouping the constituent character segments (components) of each zone into two clusters. As a result, we obtain one global centroid for all the components in the zone, and further two centroids for the two generated clusters. The above method is repeated for both foreground as well as background information. The features are generated by encoding the spatial distribution of these centroids by computing their relative angular information. These features are then fed into a SVM classifier. A PCA based feature selection phase has also been applied. Detailed experiments on Bangla and Devanagari datasets have been performed. It has been seen that the proposed methodology outperforms a recent competing method.