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

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4 Author(s)
Dreuw, P. ; Human Language Technol. & Pattern Recognition, RWTH Aachen Univ., Aachen, Germany ; Rybach, D. ; Gollan, C. ; Ney, H.

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.

Published in:

Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on

Date of Conference:

26-29 July 2009