An AI-based approach to auto-analyzing historical handwritten business documents

Jinhui Chen (), Tetsuya Takiguchi, Yasuo Takatsuki, Munehiko Itoh and Takashi Kamihigashi
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Jinhui Chen: Kobe University
Tetsuya Takiguchi: Kobe University

Journal of Computational Social Science, 2018, vol. 1, issue 1, No 11, 167-185

Abstract: Abstract Matching salient points is a key step in visual tasks. However, many of the existing feature representation methods that are widely applied to these tasks, such as scale invariant feature transform (SIFT), suffer from a lack of representation invariance. This shortcoming limits the image representation stability and salient-point matching performance, particularly when images with a great deal of noise information are being processed (e.g., historical documents). We propose a general and effective transformation approach called RIFT (reversal-invariant feature transformation) for feature-robust representation. RIFT achieves gradient binning invariance for feature extraction by transforming the conventional gradient into a polar one. Experimental results on the Kanebo database and three fine-grained reference classification datasets demonstrated that RIFT can robustly improve the performance of local descriptors for image classification without sacrificing computational efficiency.

Keywords: RIFT; Kanebo database; OCR (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s42001-017-0009-2

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