Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ink Retrieval from Handwritten Documents
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning in a multimedia document retrieval framework
IBM Systems Journal
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In this paper, we present a framework for adapting a writer independent system to a user from samples of the user's writing. The writer independent system is modeled using hidden Markov models. Training for a writer involves recomputing the topology and parameters of the hidden Markov models using the writer's data. The framework uses the writer independent system to get an initial alignment of the writer's data. The system described reduces the error rate by an average of 65%. For the results presented, no language model was used.