Improving OCR using internal document redundancy

TL;DR

This paper presents an unsupervised method that exploits internal document redundancy to improve OCR accuracy, especially on degraded printed documents, by using an extended Gaussian Mixture Model with iterative realignment and statistical testing.

Contribution

It introduces a novel unsupervised approach leveraging intra-document redundancy and an extended GMM with EM and realignment for OCR correction.

Findings

Improved OCR accuracy on degraded documents.

Effective correction of OCR outputs using internal redundancy.

Successful application to historical and archival documents.

Abstract

Current OCR systems are based on deep learning models trained on large amounts of data. Although they have shown some ability to generalize to unseen data, especially in detection tasks, they can struggle with recognizing low-quality data. This is particularly evident for printed documents, where intra-domain data variability is typically low, but inter-domain data variability is high. In that context, current OCR methods do not fully exploit each document's redundancy. We propose an unsupervised method by leveraging the redundancy of character shapes within a document to correct imperfect outputs of a given OCR system and suggest better clustering. To this aim, we introduce an extended Gaussian Mixture Model (GMM) by alternating an Expectation-Maximization (EM) algorithm with an intra-cluster realignment process and normality statistical testing. We demonstrate improvements in documents with various levels of degradation, including recovered Uruguayan military archives and 17th to mid-20th century European newspapers.