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The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial neural network (PNN) is employed to make classification. The PNN is trained with large quantities of samples collected using a bootstrap strategy. In addition, post-processing procedure including verification and integration is performed to refine the detected results. The effectiveness of the proposed method is demonstrated by experimental results.