Arabic OCR error correction using character segment correction, language modeling, and shallow morphology

Experimentation shows that character segment based correction is superior to single character correction and that language modeling boosts correction, by improving the ranking of candidate corrections, while shallow morphology had a small adverse effect.

Abstract

This paper explores the use of a character segment based character correction model, language modeling, and shallow morphology for Arabic OCR error correction. Experimentation shows that character segment based correction is superior to single character correction and that language modeling boosts correction, by improving the ranking of candidate corrections, while shallow morphology had a small adverse effect. Further, given sufficiently large corpus to extract a dictionary and to train a language model, word based correction works well for a morphologically rich language such as Arabic.