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A segmentation-free strategy based on hidden Markov models (HMMs) is presented for offline recognition of unconstrained Chinese handwriting. As the first step, handwritten textlines are converted to observation sequence by sliding windows and character segmentation stage is avoided prior to recognition. Following that, embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best...
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