A Hidden Markov Model for Alphabet-Soup Word Recognition

Recent work on the “alphabet soup” paradigm has demonstrated effective segmentation-free character-based recognition of cursive handwritten historical text documents. The approach first uses a joint boosting technique to detect potential characters the alphabet soup. A second stage uses a dynamic programming algorithm to recover the correct sequence of characters. Despite experimental success, the ad hoc dynamic programming method previously lacked theoretical justification. This paper puts the method on a sounder footing by recasting the dynamic programming as inference on an ensemble of hidden Markov models (HMMs). Although some work has questioned the use of score outputs from classifiers like boosting and support vector machines for probability estimates, experiments in this case show good results from treating shifted boosting scores as log probabilities.