Robust Document Image Dewarping Method Using Text-Lines and Line Segments

Abstract

Conventional text-line based document dewarping methods have problems when handling complex layout and/or very few text-lines. When there are few aligned text-lines in the image, this usually means that photos, graphics and/or tables take large portion of the input instead. Hence, for the robust document dewarping, we propose to use line segments in the image in addition to the aligned text-lines. Based on the assumption and observation that many of the line segments in the image are horizontally or vertically aligned in the well-rectified images, we encode this property into the cost function in addition to the text-line alignment cost. By minimizing the function, we can obtain transformation parameters for camera pose, page curve, etc., which are used for document rectification. Considering that there are many outliers in line segment directions and missed text-lines in some cases, the overall algorithm is designed in an iterative manner. At each step, we remove text components and line segments that are not well aligned, and then minimize the cost function with the updated information. Experimental results show that the proposed method is robust to the variety of page layouts.