A discriminative semi-Markov model for robust scene text recognition

We present a semi-Markov model for recognizing scene text that integrates character and word segmentation with recognition. Using wavelet features, it requires only approximate location of the text baseline and font size; no binarization or prior word segmentation is necessary. Our system is aided by a lexicon, yet it also allows non-lexicon words. To facilitate inference with a large lexicon, we use an approximate Viterbi beam search. Our system performs robustly on low-resolution images of signs containing text in fonts atypical of documents.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Allen R. Hanson, Erik G. Learned-Miller, Jerod J. Weinman
• Keywords: Approximate Viterbi Beam, Computer Vision, ICPR 2008, Prior Word Segmentation, Signs Containing Text