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The present work demonstrates a fast and improved technique for dewarpingnonlinearly warped document images. The images are first dewarped at thepage-level by estimating optimum inverse projections using curvilinearhomography. The quality of the process is then estimated by evaluating a set ofmetrics related to the characteristics of the text lines and rectilinearobjects for measuring parallelism, orthogonality, etc. These are designedspecifically to estimate the quality of the dewarping process without the needof any ground truth. If the quality is estimated to be unsatisfactory, thepage-level dewarping process is repeated with finer approximations. This isfollowed by a line-level dewarping process that makes granular corrections tothe warps in individual text-lines. The methodology has been tested on theCBDAR 2007 / IUPR 2011 document image dewarping dataset and is seen to yieldthe best OCR accuracy in the shortest amount of time, till date. The usefulnessof the methodology has also been evaluated on the DocUNet 2018 dataset withsome minor tweaks, and is seen to produce comparable results.

Multistage Curvilinear Coordinate Transform Based Document Image Dewarping using a Novel Quality Estimator