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.Comment: Proceedings of the 12th IAPR International Workshop on Document Analysis Systems (DAS2016), Santorini, Greece, April 11-14, 201