Adaptive pre-OCR cleanup of grayscale document images
Ilya Zavorin, Eugene Borovikov, Mark Turner, Luis Hernández · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
This paper describes new capabilities of ImageRefiner, an automatic image enhancement system based on machine learning (ML). ImageRefiner was initially designed as a pre-OCR cleanup filter for bitonal (black-and-white) document images. Using a single neural network, ImageRefiner learned which image enhancement transformations (filters) were best suited for a given document image and a given OCR engine, based on various image measurements (characteristics). The new release improves ImageRefiner in three major ways. First, to process grayscale document images, we have included three grayscale filters based on smart thresholding and noise filtering, as well as five image characteristics that are all byproducts of various thresholding techniques. Second, we have implemented additional ML algorithms, including a neural network ensemble and several "all-pairs" classifiers. Third, we have introduced a measure that evaluates overall performance of the system in terms of cumulative improvement of OCR accuracy. Our experiments indicate that OCR accuracy on enhanced grayscale images is higher than that of both the original grayscale images and the corresponding bitonal images obtained by scanning the same documents. We have noticed that the system's performance may suffer when document characteristics are correlated.