A new deep fuzzy based MSER model for multiple document images classification

Biswas, Kunal and Shivakumara, Palaiahnakote and Sivanthi, Sittravell and Pal, Umapada and Lu, Yue and Liu, Cheng-Lin and Ayub, Mohamad Nizam Bin (2022) A new deep fuzzy based MSER model for multiple document images classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13363. 358 – 370. ISSN 03029743, DOI https://doi.org/10.1007/978-3-031-09037-0_30.

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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. © 2022, Springer Nature Switzerland AG.

Item Type: Article
Funders: TIH, Natural Science Foundation of Shanghai [Grant No; 19ZR1415900], National Natural Science Foundation of China [Grant No; 62176091], National Key Research and Development Program of China [Grant No; 2020AAA0107903], Indian Statistical Institute
Uncontrolled Keywords: Character recognition; Deep learning; Information retrieval systems; Text processing; Document classification; Document image analysis; Document image understanding; Document images; Document understanding; Handwritten document; Handwritten document understanding; Scene Text; Scene text recognition; Text recognition; Image classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 01 Nov 2023 07:07
Last Modified: 01 Nov 2023 07:07
URI: http://eprints.um.edu.my/id/eprint/43537

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