Turkish Document Image Classification

Nar M. T., Durukan G., Özcan A., Çakıl L., Kara H., İlhan Omurca S.

International Conference on Advanced Engineering, Technology and Applications (ICAETA), Catania, Italy, 24 - 25 May 2024, pp.1, (Full Text)

  • Publication Type: Conference Paper / Full Text
  • City: Catania
  • Country: Italy
  • Page Numbers: pp.1
  • Kocaeli University Affiliated: Yes

Abstract

Document image classification has gained extensive attention

due to the rising number and types of scanned documents. Multimodal

architectures, processing image and text simultaneously, leverage

the strengths of each modality. This study explores an efficient neural

architecture for classifying scanned documents in a private company.

The effectiveness of CNN-based deep learning and OCR algorithms in

extracting textual and visual features is investigated. Different feature

fusion methods are applied in the next stage to combine these extracted

features. A multi-modal document image classifier is developed for companies

managing a large number of scanned documents, delivering superior

performance even with fewer and faint documents.