A Simple and Practical Approach to Improve Misspellings in OCR Text
This addresses the challenge of improving OCR accuracy for text processing applications, but it is incremental as it builds on traditional methods with a limited study.
The paper tackled the problem of correcting non-word errors in OCR text, particularly split and merge errors, by developing an unsupervised method that achieved a sizable improvement in correction rates.
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.