  This paper explores the use of a learned classifier for post-OCR text
correction. Experiments with the Arabic language show that this approach, which
integrates a weighted confusion matrix and a shallow language model, improves
the vast majority of segmentation and recognition errors, the most frequent
types of error on our dataset.
