Script identification in a handwritten document image using texture features

Script identification for handwritten document image is an open document analysis problem. In this paper, we propose an approach to script identification for documents containing handwritten text using the texture features. The texture features are extracted based on the co-occurrence histograms of wavelet decomposed images, which capture the information about relationships between each high frequency subband and that in low frequency subband of the transformed image at the corresponding level. The correlation between the subbands at the same resolution exhibits a strong relationship, indicating that this information is significant for characterizing a texture. This scheme is tested on seven Indian language scripts along with English. Our method is robust to the skew generated in the process of scanning a document and also to the varying coverage of text. The experimental results demonstrate the effectiveness of the texture features in identification of handwritten scripts. The experiments are also performed by considering the multiple writers.