OCR Error Correction Using Character Correction and Feature-Based Word   Classification
  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. 
Ido Kissos, Nachum Dershowitz
cs.IR cs.CL