Off-line handwriting identification using HMM based recognizers

An off-line, text independent system for writer identification using hidden Markov model (HMM) based recognizers is described. For each writer we build an individual recognizer and train it on text lines written by that writer. A text line of unknown origin is presented to each of these recognizers. As a result we get, from each recognizer, a transcription including the log-likelihood score for the considered input. We rank all scores, and based on the assumption that the recognizer with the highest log-likelihood is the one that has been trained using text lines of this writer, we assign the text line to the writer whose score ranks first. We tested our system using over 2,200 text lines from 50 writers and have in 94.47% of all cases correctly identified the writer. Using a simple confidence measure to define a rejection mechanism, we achieved an error rate of 0% by rejecting 15% of the results.