Turkish Document Image Classification

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

International Conference on Advanced Engineering, Technology and Applications (ICAETA), Catania, İtalya, 24 - 25 Mayıs 2024, ss.1, (Tam Metin Bildiri)

• Yayın Türü: Bildiri / Tam Metin Bildiri
• Basıldığı Şehir: Catania
• Basıldığı Ülke: İtalya
• Sayfa Sayıları: ss.1
• Kocaeli Üniversitesi Adresli: Evet

Özet

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.