Stroke extraction from grayscale images of financial documents based on figures of importance

This paper describes a new method for stroke extraction from grayscale images based on an effective local adaptive kernel technique that assigns a mark for each pixel in the image signifying its relative importance with respect to neighboring pixels. The assigned marks are based on accumulating evidence of the relative importance of each pixel based on multiple comparisons with neighboring pixels under different windows. A global threshold is then applied to the marks produced in the first step to reach a conclusion as to whether a pixel ought to be preserved as part of a stroke or be dropped as part of the background. This technique effectively combines local and global thresholding techniques and shows promising performance especially on images with non-uniform backgrounds or embedded light strokes. The advantage of this scheme is also demonstrated in terms of superior overall amount recognition performance for financial document processing systems utilizing it.