Using HMM based recognizers for writer identification and verification

In this paper, we use HMM based recognizers for the identification and verification of persons based on their handwriting. For each writer, we build an individual recognizer and train it on text lines of that writer. This gives us recognizers that are experts on the handwriting of exactly one writer. In the identification or verification phase, a text line of unknown origin is presented to each of these recognizers and each one returns a transcription that includes the log-likelihood score for the considered input. These scores are sorted and the resulting ranking is used for both identification and verification. In an identification experiment in 96.56% of all cases the writer out of a set of 100 writers is correctly identified. Second, in a verification experiment using over 8,600 text lines from 120 writers an equal error rate (EER) of about 2.5% is achieved.