Instance-Based skew estimation of document images by a combination of variant and invariant

Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

Research output: Contribution to conferencePaperpeer-review

Abstract

A novel technique for estimating geometric deformations is proposed and applied to document skew (i.e., rotation) estimation. The proposed method possesses two novel properties. First, the proposed method estimates the skew angles at individual connected components. Those skew angles are then voted to determine the skew angle of the entire document. Second, the proposed method is based on instancebased learning. Specifically, a rotation variant and a rotation invariant are learned, i.e., stored as instances for each character category, and referred for estimating the skew angle very efficiently. The result of a skew estimation experiment on 55 document images has shown that the skew angles of 54 document images were successfully estimated with errors smaller than 2.0 degree. The extension for estimating perspective deformation is also discussed for the application to camera-based OCR.

Original languageEnglish
Pages53-60
Number of pages8
Publication statusPublished - 2007
Event2nd International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2007 - Curitiba, Brazil
Duration: Sep 22 2007Sep 22 2007

Other

Other2nd International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2007
Country/TerritoryBrazil
CityCuritiba
Period9/22/079/22/07

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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