TY - CONF T1 - Identifying Script on Word-Level with Informational Confidence T2 - 8th Int. Conf. on Document Analysis and Recognition Y1 - 2005 A1 - Jaeger,Stefan A1 - Ma,Huanfeng A1 - David Doermann AB - In this paper, we present a multiple classifier system for script identification. Applying a Gabor filter analysis of textures on word-level, our system identifies Latin and non-Latin words in bilingual printed documents. The classifier system comprises four different architectures based on nearest neighbors, weighted Euclidean distances, Gaussian mixture models, and support vector machines. We report results for Arabic, Chinese, Hindi, and Korean script. Moreover, we show that combining informational confidence values using sum-rule can consistently outperform the best single recognition rate. JA - 8th Int. Conf. on Document Analysis and Recognition ER -