公开(公告)号：US11132572B2

公开(公告)日：2021-09-28

申请号：US16562574

申请日：2019-09-06

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Zhenghui Zhang ,  Bing Wu ,  Lingtao Zeng ,  Qiuzhen Wang ,  Jinhua Zheng

IPC: G06K9/20 ,  G06K9/00 ,  G06N3/08 ,  G06T5/00

Abstract: Disclosed is a method and system for splicing and restoring shredded paper based on an extreme learning machine (“ELM”). The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the sample; training an ELM neural network model according to the feature data to obtain a trained neural network model (“TNNM”); acquiring a shredded paper test sample to be spliced; extracting feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting, by the TNNM, a shredded piece with a highest degree of coincidence with the first piece; determining whether the shredded piece is correctly spliced to the first piece; if yes, splicing shredded paper until all shredded paper is spliced and restored; if not, adopting manual marking, and continuing to select, by the TNNM, shredded paper with a highest degree of coincidence with the first piece.

公开(公告)号：US20200097748A1

公开(公告)日：2020-03-26

申请号：US16562574

申请日：2019-09-06

Applicant: XIANGTAN UNIVERSITY

Inventor： Juan Zou ,  Zhenghui Zhang ,  Bing Wu ,  Lingtao Zeng ,  Qiuzhen Wang ,  Jinhua Zheng

IPC: G06K9/20 ,  G06N3/08 ,  G06K9/00

Abstract: The present invention discloses a method and system for splicing and restoring shredded paper based on an extreme learning machine. The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the training sample; training an extreme learning machine neural network model according to the left and right boundary feature data, to obtain a trained neural network model; acquiring a shredded paper test sample to be spliced; extracting left and right boundary feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting shredded paper with a highest degree of coincidence with the first piece of to-be-spliced shredded paper by the trained neural network model; determining whether the shredded paper with the highest degree of coincidence is correctly spliced to the first piece of to-be-spliced shredded paper; if yes, splicing shredded paper until all the shredded paper is spliced and restored; and if not, adopting manual marking, and continuing to select shredded paper with a highest degree of coincidence with the first piece of to-be-spliced shredded paper by the trained neural network model. The method and system for splicing and restoring shredded paper based on an extreme learning machine can well splice and restore shredded paper quickly.Disclosed is a method and system for splicing and restoring shredded paper based on an extreme learning machine (“ELM”). The method includes: acquiring a shredded paper training sample to be spliced; extracting left and right boundary feature data of the sample; training an ELM neural network model according to the feature data to obtain a trained neural network model (“TNNM”); acquiring a shredded paper test sample to be spliced; extracting feature data of the test sample; selecting a first piece of to-be-spliced shredded paper; selecting, by the TNNM, a shredded piece with a highest degree of coincidence with the first piece; determining whether the shredded piece is correctly spliced to the first piece; if yes, splicing shredded paper until all shredded paper is spliced and restored; if not, adopting manual marking, and continuing to select, by the TNNM, shredded paper with a highest degree of coincidence with the first piece.