Writer Adaptive Training and Writing Variant Model Refinement for Offline Arabic Handwriting Recognition

Authors:
Philippe Dreuw;David Rybach;Christian Gollan;Hermann Ney
Affiliations:
-;-;-;-
Venue:
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Year:
2009

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Abstract

We present a writer adaptive training and writer clustering approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations. Additionally, a writing variant model refinement for specific writing variants is proposed.Current approaches try to compensate the impact of different writing styles during preprocessing and normalization steps.Writer adaptive training with a CMLLR based feature adaptation is used to train writer dependent models. An unsupervised writer clustering with Bayesian information criterion based stopping condition for a CMLLR based feature adaptation during a two-pass decoding process is used to cluster different handwriting styles of unknown test writers.The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database.