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Precise homography estimation between multiple images is a pre-requisite formany computer vision applications. One application that is particularlyrelevant in today's digital era is the alignment of scanned or camera-captureddocument images such as insurance claim forms for information extraction.Traditional learning based approaches perform poorly due to the absence of anappropriate gradient. Feature based keypoint extraction techniques forhomography estimation in real scene images either detect an extremely largenumber of inconsistent keypoints due to sharp textual edges, or produceinaccurate keypoint correspondences due to variations in illumination andviewpoint differences between document images. In this paper, we propose anovel algorithm for aligning scanned or camera-captured document images usingcharacter based keypoints and a reference template. The algorithm is both fastand accurate and utilizes a standard Optical character recognition (OCR) enginesuch as Tesseract to find character based unambiguous keypoints, which areutilized to identify precise keypoint correspondences between two images.Finally, the keypoints are used to compute the homography mapping between atest document and a template. We evaluated the proposed approach forinformation extraction on two real world anonymized datasets comprised ofhealth insurance claim forms and the results support the viability of theproposed technique.

Character Keypoint-based Homography Estimation in Scanned Documents for Efficient Information Extraction