TY - GEN
T1 - Towards an intelligent fuzzy-fusion model for identity document classification
AU - Khandan, Nouna
AU - Beheshti, Amin
AU - Farhood, Helia
AU - Pooshideh, Matineh
AU - Simpson, Mike
AU - Gatland, Nick
PY - 2021
Y1 - 2021
N2 - Digitization, i.e., the process of converting information into a digital format, may provide various opportunities and challenges for businesses. In this context, one of the main challenges would be to accurately classify numerous scanned documents uploaded every day by customers as usual business processes. The current study has proposed a robust fusion model to define the type of identity documents accurately. The proposed approach is based on two different methods in which images are classified based on their visual features and text features. A novel model based on statistics and regression has been proposed to calculate the confidence level for the feature-based classifier. A fuzzy-mean fusion model has been proposed to combine the classifier results based on their confidence score. The proposed approach has been implemented using Python and experimentally validated on synthetic and real-world datasets. The performance is evaluated using the Receiver Operating Characteristic (ROC) curve analysis.
AB - Digitization, i.e., the process of converting information into a digital format, may provide various opportunities and challenges for businesses. In this context, one of the main challenges would be to accurately classify numerous scanned documents uploaded every day by customers as usual business processes. The current study has proposed a robust fusion model to define the type of identity documents accurately. The proposed approach is based on two different methods in which images are classified based on their visual features and text features. A novel model based on statistics and regression has been proposed to calculate the confidence level for the feature-based classifier. A fuzzy-mean fusion model has been proposed to combine the classifier results based on their confidence score. The proposed approach has been implemented using Python and experimentally validated on synthetic and real-world datasets. The performance is evaluated using the Receiver Operating Characteristic (ROC) curve analysis.
KW - document classification
KW - object detection
KW - fuzzy fusion
KW - SIFT
UR - https://www.scopus.com/pages/publications/85122633099
U2 - 10.1145/3487664.3487738
DO - 10.1145/3487664.3487738
M3 - Conference proceeding contribution
AN - SCOPUS:85122633099
T3 - ACM International Conference Proceeding Series
SP - 523
EP - 531
BT - 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021 - Proceedings
A2 - Pardede, Eric
A2 - Santiago, Maria-Indrawan
A2 - Haghighi, Pari Delir
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
A2 - Kotsis, Gabriele
PB - Association for Computing Machinery
CY - New York, NY
T2 - 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021 and the 19th International conference on Advances in Mobile Computing & Multimedia Intelligence, MoMM2021
Y2 - 29 November 2021 through 1 December 2021
ER -