Why multiple document image binarizations improve OCR

Abstract

Our previous work has shown that the error correction of optical character recognition (OCR) on degraded historical machine-printed documents is improved with the use of multiple information sources and multiple OCR hypotheses including from multiple document image binarizations. The contributions of this paper are in demonstrating how diversity among multiple binarizations makes those improvements to OCR accuracy possible. We demonstrate the degree and breadth to which the information required for correction is distributed across multiple binarizations of a given document image. Our analysis reveals that the sources of these corrections are not limited to any single binarization and that the full range of binarizations holds information needed to achieve the best result as measured by the word error rate (WER) of the final OCR decision. Even binarizations with high WERs contribute to improving the final OCR. For the corpus used in this research, fully 2.68% of all tokens are corrected using hypotheses not found in the OCR of the binarized image with the lowest WER. Further, we show that the higher the WER of the OCR overall, the more the corrections are distributed among all binarizations of the document image.