Maximizing Edit Distance Accuracy with Hidden Conditional Random Fields

Handwriting recognition aims at predicting a sequence of characters from an image of a handwritten text. Main approaches rely on learning statistical models such as Hidden Markov Models or Conditional Random Fields, whose quality is measured through character and word error rates while they are usually not trained to optimize such criterion. We propose an efficient method for learning Hidden Conditional Random Fields to optimize the error rate within the large margin framework.