Identifying script on word-level with informational confidence

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 classfier 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.