Scene text recognition using sparse coding based features

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. HSC features are extracted by computing sparse codes with dictionaries, which are learned from data using K-SVD, and aggregating perpixel 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 result is obtained by searching for the word which corresponds 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 the ICDAR2003 and SVT datasets demonstrate that the HSC-based scene text recognition method outperforms the HOG-based method significantly and achieves the state-of-the-art performance.