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Current methods in scene character recognition heavily rely on discriminative power of local features, such as HoG, SIFT, Shape Contexts (SC), Geometric Blur (GB), etc. One of the problems with this approach is that the local features are rasterized in an ad hoc manner into a single vector perturbing thus spatial correlations that carry crucial information. To eliminate this feature dependency and associated problems, we propose a holistic solution as follows: For each character to be recognized, we stack a set of training images to form a 3-mode tensor. Each training tensor is then decomposed into a linear superposition of 'k' rank-1 matrices, whereby the rank-1 matrices form a basis, spanning solution subspace of the character class. For a test image to be classified, we obtain projections onto the pre-computed rank-1 bases of each class, and recognize it as the class for which inner-product of mixing vectors is maximized. We use challenging natural scene character datasets, namely Chars74K, ICDAR2003, and SVT-CHAR. We achieve results better than several baseline methods based on local features (e.g. HoG) and show leave-random-one-out-cross validation yield even better recognition performance, justifying thus our intuition of the importance of featureindependency and preservation of spatial correlations in recognition.

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Background Nature represents a basic source of medicinal scaffolds that can develop into potent drugs used in the treatment of many diseases. Aim The present study was planned to evaluate the combined effects of polyherbal methanolic extract of the herbs (fruit of capsicum, bark of cinnamon, rhizome of turmeric and rhizome of ginger) that were individually well known for their analgesic and anti-inflammatory activities. Furthermore, we aimed to develop hydrogel formulation of this polyherbal extract and to characterize and evaluate its analgesic and anti-inflammatory potential. Materials and Methods Zingiber officinale (R.), Capsicum annuum (L.), Curcuma longa (L.), and Cinnamomum verum (J.) polyherbal extract (GCTC) was prepared by maceration and evaluated for analgesic and anti-inflammatory potential. Then, two different types of hydrogel formulation were prepared. One is pH-based hydrogel in which carbopol-940 was used and the other is temperature-based gel in which methocel-K100 was used as gelling agent. Different concentrations of polyherbal extract (GCTC), at 1%, 3% and 5%, were used in hydrogel formulation. These prepared hydrogel formulations were characterized and evaluated for analgesic and anti-inflammatory potential. Results Results show that polyherbal extract and all the developed formulations of polyherbal extract (GCTC), at concentrations of 1%, 3% and 5%, have significant analgesic and anti-inflammatory effects with good appearance, homogeneity, spreadability, extrudability and stability. Conclusion It was concluded from this project that polyherbal extract (GCTC) and its hydrogel have significant analgesic and anti-inflammatory potential.