A Simple and Practical Approach to Improve Misspellings in OCR Text

TL;DR

This paper presents an unsupervised method to improve OCR text correction by effectively handling non-word errors, including insertions, deletions, substitutions, transpositions, and boundary errors, demonstrating significant improvements in correction rates.

Contribution

It introduces a novel unsupervised approach capable of correcting complex OCR errors like run-on and split errors, surpassing traditional N-gram methods.

Findings

Significant improvement in OCR error correction rates

Effective handling of run-on and split errors

Assessment on a limited study shows promising results

Abstract

The focus of our paper is the identification and correction of non-word errors in OCR text. Such errors may be the result of incorrect insertion, deletion, or substitution of a character, or the transposition of two adjacent characters within a single word. Or, it can be the result of word boundary problems that lead to run-on errors and incorrect-split errors. The traditional N-gram correction methods can handle single-word errors effectively. However, they show limitations when dealing with split and merge errors. In this paper, we develop an unsupervised method that can handle both errors. The method we develop leads to a sizable improvement in the correction rates. This tutorial paper addresses very difficult word correction problems - namely incorrect run-on and split errors - and illustrates what needs to be considered when addressing such problems. We outline a possible approach and assess its success on a limited study.