Benchmarking Keypoint Filtering Approaches for Document Image Matching

Royer, Emilien; Chazalon, Joseph; Rusiñol, Marçal; Bouchara, Frédéric

doi:10.1109/icdar.2017.64

Cited by 4 publications

(1 citation statement)

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“…Since centroid computation at different orientations suffers from lack of precision, these features cannot be used for our task which requires exactness. [10] used structures in the text document like punctuation characters as keypoints for document mosaicing, while Royer et al [11] explored keypoint selection methods which reduce the number of extracted keypoints for improved document image matching. Recently, deep neural networks have become popular to obtain powerful feature descriptors [12]- [15] compared with the traditional descriptors.…”

Section: A Related Workmentioning

confidence: 99%

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

Mahajan¹,

Sharma²,

Vig³

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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Precise homography estimation between multiple images is a pre-requisite for many computer vision applications. One application that is particularly relevant in today's digital era is the alignment of scanned or cameracaptured document images such as insurance claim forms for information extraction. Traditional learning based approaches perform poorly due to the absence of an appropriate gradient. Feature based keypoint extraction techniques for homography estimation in real scene images either detect an extremely large number of inconsistent keypoints due to sharp textual edges, or produce inaccurate keypoint correspondences due to variations in illumination and viewpoint differences between document images. In this paper, we propose a novel algorithm for aligning scanned or camera-captured document images using character based keypoints and a reference template. The algorithm is both fast and accurate and utilizes a standard Optical character recognition (OCR) engine such as Tesseract to find character based unambiguous keypoints, which are utilized to identify precise keypoint correspondences between two images. Finally, the keypoints are used to compute the homography mapping between a test document and a template. We evaluated the proposed approach for information extraction on two real world anonymized datasets comprised of health insurance claim forms and the results support the viability of the proposed technique.

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Section: A Related Workmentioning

confidence: 99%