TY - GEN
T1 - A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification
AU - Biswas, Kunal
AU - Shivakumara, Palaiahnakote
AU - Sivanthi, Sittravell
AU - Pal, Umapada
AU - Lu, Yue
AU - Liu, Cheng Lin
AU - Ayub, Mohamad Nizam Bin
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate.
AB - Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate.
KW - Document classification
KW - Document image analysis
KW - Document image understanding
KW - Handwritten documents understanding
KW - Scene text recognition
UR - https://www.scopus.com/pages/publications/85131959528
U2 - 10.1007/978-3-031-09037-0_30
DO - 10.1007/978-3-031-09037-0_30
M3 - 会议稿件
AN - SCOPUS:85131959528
SN - 9783031090363
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 358
EP - 370
BT - Pattern Recognition and Artificial Intelligence - 3rd International Conference, ICPRAI 2022, Proceedings
A2 - El Yacoubi, Mounîm
A2 - Granger, Eric
A2 - Yuen, Pong Chi
A2 - Pal, Umapada
A2 - Vincent, Nicole
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022
Y2 - 1 June 2022 through 3 June 2022
ER -