A Novel 2D Deep Convolutional Neural Network for Multimodal Document Categorization
Abkrakhmanov R. Elubaeva A. Turymbetov T. Nakhipova V. Turmaganbetova S. Ikram Z.
2023Science and Information Organization
International Journal of Advanced Computer Science and Applications
2023#14Issue 7720 - 728 pp.
Digitized documents are increasingly becoming prevalent in various industries. The ability to accurately classify these documents is critical for efficient and effective management. However, digitized documents often come in different formats, making document classification a challenging task. In this paper, we propose a multimodal deep learning approach for digitized document classification. The proposed approach combines both text and image modalities to improve classification accuracy. The model architecture consists of a convolutional neural network (CNN) for image processing and a recurrent neural network (RNN) for text processing. The output features from the two modalities are then merged using a fusion layer to generate the final classification result. The proposed approach is evaluated on a dataset of digitized documents from various industries, including finance, healthcare, and legal fields. The experimental results demonstrate that the multimodal approach outperforms single-modality approaches, achieving high accuracy for document classification. The proposed model has significant potential for applications in various industries that rely heavily on document management systems. For example, in the finance industry, the proposed model can be used to classify loan applications or financial statements. In the healthcare industry, the model can classify patient records, medical images, and other medical documents. In the legal industry, the model can classify legal documents, contracts, and court filings. Overall, the proposed multimodal deep learning approach can significantly improve document classification accuracy, thus enhancing the efficiency and effectiveness of document management systems.
artificial intelligence , classification , deep learning , document categorization , machine learning , Scanned documents
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International University of Tourism and Hospitality, Turkistan, Kazakhstan
International University of Tourism and Hospitality, Turkistan, Kazakhstan
Khoja Akhmet Yassawi International Kazakh, Turkish University, Turkistan, Kazakhstan
Zhumabek Akhmetuly Tashenev University, Shymkent, Kazakhstan
NCJSC «S.Seifullin Kazakh Agro Technical Research University», Astana, Kazakhstan
BTS Digital, Astana, Kazakhstan
International University of Tourism and Hospitality
International University of Tourism and Hospitality
Khoja Akhmet Yassawi International Kazakh
Zhumabek Akhmetuly Tashenev University
NCJSC «S.Seifullin Kazakh Agro Technical Research University»
BTS Digital
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