This paper tries to model spatial layout beyond the traditional spatial pyramid (SP) in the coding/pooling scheme for scene text character recognition. Specifically, we propose a novel method to build a dictionary called spatiality embedded dictionary (SED) in which each codeword represents a particular character stroke and is associated with a local response region. The promising results outperform other state-of-the-art algorithms.

This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN based interpolating path between the source and target views is built to model the correlation of cross-view data. The promising results outperform other state-of-the-art methods.

In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.