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We present a framework of adaptive (self-training) semi-supervised learning as applied to the problem of handwriting recognition. Each problem instance itself is treated as a set of unlabeled "training'' data; a general model, trained on a set of labeled data, is adapted into an appropriate problem specific model. Learning is continued until convergence is reached, yielding better results than the generalized model alone. An implementation of the framework was tested on English and Arabic handwritten documents. The initial supervised learning model gave word recognition performance of 81% and 67% for English and Arabic respectively. The subsequent semi-supervised learning adjustments yielded 86% and 77% word recognition performance for English and Arabic respectively.