Abstract

We present an OCR-driven writer identification algorithm in this paper. Our algorithm learns writer-specific characteristics more precisely from explicit character alignment using the Viterbi algorithm and shows significant reduction of close-set writer identification error rates, compared with the GMM-based method. With writers' identities retrieved, we improve the performance of handwriting recognition using the HMM trained adapted on the training data of that writer. In our system, writer identification and OCR are highly interactive. They improve the performance of each other and thus show close approximation of supervised text-dependent writer identification and writer-dependent HMM handwriting.

Date

2011

Authors

Huaigu Cao, Rohit Prasad, Prem Natarajan

Conference

2011 International Conference on Document Analysis and Recognition

Pages

739-743

Publisher

IEEE