Confidence-Based Discriminative Training for Model Adaptation in Offline Arabic Handwriting Recognition

We present a novel confidence-based discriminative training for model adaptation approach for an HMM based Arabic handwriting recognition system to handle different handwriting styles and their variations.Most current approaches are maximum-likelihood trained HMM systems and try to adapt their models to different writing styles using writer adaptive training, unsupervised clustering, or additional writer specific data.Discriminative training based on the Maximum Mutual Information criterion is used to train writer independent handwriting models. For model adaptation during decoding, an unsupervised confidence-based discriminative training on a word and frame level within a two-pass decoding process is proposed. Additionally, the training criterion is extended to incorporate a margin term.The proposed methods are evaluated on the IFN/ENIT Arabic handwriting database, where the proposed novel adaptation approach can decrease the word-error-rate by 33% relative.