Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting

TL;DR

This paper presents a novel ensemble method combining cross fold training and confidence voting to significantly improve OCR accuracy on early printed books, reducing character errors by up to 50%.

Contribution

It introduces a new ensemble approach that leverages multiple models and confidence voting to enhance OCR performance on challenging early printed texts.

Findings

Error rates reduced by up to 50%

Outperforms standard OCR methods significantly

Effective on seven early printed books

Abstract

In this paper we introduce a method that significantly reduces the character error rates for OCR text obtained from OCRopus models trained on early printed books. The method uses a combination of cross fold training and confidence based voting. After allocating the available ground truth in different subsets several training processes are performed, each resulting in a specific OCR model. The OCR text generated by these models then gets voted to determine the final output by taking the recognized characters, their alternatives, and the confidence values assigned to each character into consideration. Experiments on seven early printed books show that the proposed method outperforms the standard approach considerably by reducing the amount of errors by up to 50% and more.