Combining multiple thresholding binarization values to improve OCR output

For noisy, historical documents, a high optical character recognition (OCR) word error rate (WER) can render the OCR text unusable. Since image binarization is often the method used to identify foreground pixels, a significant body of research has sought to improve image-wide binarization directly. Instead of relying on any one imperfect binarization technique, our method incorporates information from multiple global threshold binarizations of the same image to improve text output. Using a new corpus of 19th century newspaper grayscale images for which the text transcription is known, we observe WERs of 13.8% and higher using current binarization techniques and a state-of-the-art OCR engine. Our novel approach combines the OCR outputs from multiple thresholded images by aligning the text output and producing a lattice of word alternatives from which a lattice word error rate (LWER) is calculated. Our results show a LWER of 7.6% when aligning two threshold images down to a LWER of 6.8% when aligning five. From the word lattice we commit to one hypothesis by applying the methods of Lund et al. (2011) achieving 8.41% WER, a 39.1% reduction in error rate relative to the performance of the original OCR engine on this data set.