Document Type : Original/Review Paper

Author

• Mohammad Hossein Khosravi

Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

Abstract

Document Image Quality Assessment (DIQA) is critical for ensuring the reliability of downstream applications such as Optical Character Recognition (OCR), digital archiving, and automated document workflows. In this paper, we propose a deep learning-based DIQA framework using a Siamese neural network architecture with an InceptionV3 backbone. Our model leverages a composite loss function that combines linear regression loss with a monotonic ranking constraint to jointly optimize for score-level accuracy and perceptual consistency. Unlike prior works that rely on handcrafted features or narrow degradation types, our approach generalizes across diverse distortions commonly observed in scanned and photographed documents. Experimental results on the SOC and SmartDoc-QA datasets demonstrate that the proposed model exhibits a strong correlation with OCR accuracy, achieving SROCC values of 0.952 and 0.873, respectively, and outperforming several state-of-the-art DIQA methods.

Keywords

• Document Image Quality Assessment (DIQA)
• Siamese Network
• InceptionV3
• Deep Learning
• Custom Loss Functions

Main Subjects

• H.3.2.2. Computer vision