method: PIONEER2013-04-06

Authors: Jerod Weinman

Description: The Grinnell PIONEER system (Probabilistic Integrator of Noisy Evidence Enabling Reading) of Weinman et al. first segments each word image into superpixels with a mixture of position-conditional regressors. For binarization, a logistic regression classifier then chooses the mixture component(s) most likely to be text. We robustly fit second degree polynomials to the tops and bottoms of binarized characters with an EM algorithm. Word images are then normalized to a horizontal, linear orientation with a thin-plate spline. Finally, a discriminative semi-Markov model jointly segments and recognizes characters in the normalized image using steerable pyramid features, character bigrams, and a large lexicon. [1,2]

[1] Jerod J. Weinman, Zachary Butler, Dugan Knoll, and Jacqueline Feild. Toward integrated scene text reading. IEEE Trans. on Pattern Anal. Mach. Intell., 2013. Revision under review.

[2] Jerod J. Weinman, Erik Learned-Miller, and Allen Hanson. A discriminative semi-Markov model for robust scene text recognition. In Proc. Intl. Conf. on Pattern Recognition, Dec 2008.