A novel seal imprint verification method based on analysis of difference images and symbolic representation

Authors:
Xiaoyang Wang;Youbin Chen
Affiliations:
Graduate School at Shenzhen, Tsinghua University, Shenzhen, P.R. China;Graduate School at Shenzhen, Tsinghua University, Shenzhen, P.R. China
Venue:
IWCF'10 Proceedings of the 4th international conference on Computational forensics
Year:
2010

Citing 8
Cited 0

Unconstrained seal imprint verification using attributed stroke graph matching

Pattern Recognition
Symbolic clustering using a new dissimilarity measure

Pattern Recognition
A system for automatic Chinese seal imprint verification

ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Automatic Verification System for Seal Imprints on Japanese Bankchecks

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Automatic Seal Verification by Evaluating Positive Cost

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Multivalued type proximity measure and concept of mutual similarity value useful for clustering symbolic patterns

Pattern Recognition Letters
Automatic Seal Imprint Verification System for Bankcheck Processing

ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Online Signature Verification and Recognition: An Approach Based on Symbolic Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel seal imprint verification method with difference image based statistical feature extraction and symbolic representation based classification. After several image processing procedures including seal imprint extraction and seal registration with the model seal imprint, the statistical feature was extracted from difference images for the pattern classification system of seal verification. Symbolic representation method which requires only genuine samples in the learning phase was used to classify genuine and fake seal imprints. We built up a seal imprint image database for training the seal verification algorithms and testing the proposed verification system. Experiments showed that the symbolic representation method was superior to traditional SVM classifier in this task. Experiments also showed that our statistical feature was very powerful for seal verification application.