@InProceedings{10.1007/978-3-319-11758-4_39, author="Jetley, Saumya and Mehrotra, Kapil and Vaze, Atish and Belhe, Swapnil", editor="Campilho, Aur{\'e}lio and Kamel, Mohamed", title="Multi-script Identification from Printed Words", booktitle="Image Analysis and Recognition", year="2014", publisher="Springer International Publishing", address="Cham", pages="359--368", abstract="In today's multi-script scenario, documents contain page, paragraph, line and up to word level intermixing of different scripts. We need a script recognition approach that can perform well even at the lowest semantically-valid level of words so as to serve as a generic solution. The present paper proposes a combination of Histogram of Oriented Gradients (HoG) and Local Binary Patterns (LBP), extracted over words, to capture the unique and discriminative structural formations of different scripts. Tested over MILE printed-word data set, this concatenated feature descriptor yields a state-of-the-art average recognition accuracy of 97.4 {\%} over a set of 11 Indian scripts.", isbn="978-3-319-11758-4" }