Texture analysis for robust reading systems

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This thesis focuses on the use of texture analysis for Robust Reading Systems. In this thesis the use of texture analysis for text-images is explored. An in depth analysis of the established Local Binary Pattern (LBP) descriptor is presented. The LBP descriptors are used in word-spotting and achieves top performance among learning-free methods. A custom variant called Sparse Radial Sampling LBP is developed to exploit the unique properties of text and is used to achieve state-of-the-art performance in writer identification. The same feature descriptors are used in conjunction with deep Neural Networks in order to address successfully the problem of script and language identification in multiple modalities.