A writer identification and verification system using HMM based recognizers

Abstract

In this paper, an off-line, text independent system for writer identification and verification of handwritten text lines using Hidden Markov Model (HMM) based recognizers is presented. For each writer, an individual recognizer is built and trained on text lines of that writer. This results in a number of recognizers, each of which is an expert on the handwriting of exactly one writer. In the identification and 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 generated output. These scores are sorted and the resulting ranking is used for both identification and verification. Several confidence measures are defined on this ranking. The proposed writer identification and verification system is evaluated using different experimental setups.