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In this paper, we propose an effective scene text recognition method usingsparse coding based features, called Histograms of Sparse Codes (HSC) features.For character detection, we use the HSC features instead of using theHistograms of Oriented Gradients (HOG) features. The HSC features are extractedby computing sparse codes with dictionaries that are learned from data usingK-SVD, and aggregating per-pixel sparse codes to form local histograms. Forword recognition, we integrate multiple cues including character detectionscores and geometric contexts in an objective function. The final recognitionresults are obtained by searching for the words which correspond to the maximumvalue of the objective function. The parameters in the objective function arelearned using the Minimum Classification Error (MCE) training method.Experiments on several challenging datasets demonstrate that the proposedHSC-based scene text recognition method outperforms HOG-based methodssignificantly and outperforms most state-of-the-art methods.

Robust Scene Text Recognition Using Sparse Coding based Features