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