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This paper proposes a novel hybrid method to robustly and accurately localize texts in natural scene images. A text region detector is designed to generate a text confidence map, based on which text components can be segmented by local binarization approach. A conditional random field (CRF) model, considering the unary component property as well as binary neighboring component relationship, is then...
The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum classification error (MCE). This paper proposes a new prototype learning algorithm based on the minimization of a conditional log-likelihood loss (CLL), called log-likelihood of margin (LOGM). A regularization term is added...
In this paper, we present a robust system to accurately detect and localize texts in natural scene images. For text detection, a region-based method utilizing multiple features and cascade AdaBoost classifier is adopted. For text localization, a window grouping method integrating text line competition analysis is used to generate text lines. Then within each text line, local binarization is used to...
Density estimation in high-dimensional data spaces is a challenge due to the sparseness of data which is known as ldquothe curse of dimensionalityrdquo. Researchers often resort to low-dimensional subspaces for such tasks, while discard the distribution in the complementary subspace. In this paper, we propose a new mixture density model based on pooled subspace. In our method, the Gaussian components...
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