Deep-shallow feature fusion for assessing document image quality

The purpose of this study is to develop a hybrid approach for document image quality assessment (DIQA) that effectively integrates traditional handcrafted features with deep learning-based features to improve accuracy, robustness and generalization across various document types and degradations. The proposed method extracts handcrafted features such as Local Binary Patterns, Gray-Level Co-occurrence Matrix, Gabor filters, Scale-Invariant Feature Transform and Haar-like features, as well as deep features using pre-trained convolutional neural networks (EfficientNet, Visual Geometry Group Network (VGGNet), InceptionV3 and ResNet). These features are fused into a composite vector, followed by dimensionality reduction using Principal Component Analysis. A Random Forest regression model is then trained to predict image quality scores. The method is evaluated on the Smartdoc-QA dataset. Experimental results demonstrate that the proposed method achieves higher correlation with ground-truth scores than several state-of-the-art methods. The integration of traditional and deep features enhances robustness against various distortions such as blur, noise, skew and lighting variations, and results in improved Pearson Linear Correlation Coefficient and Spearman Rank Order Correlation Coefficient metrics. This paper presents a novel hybrid DIQA framework that leverages both low-level structural/textural cues and high-level semantic representations. It is among the first to systematically combine shallow and deep features with dimensionality reduction and regression to form an end-to-end DIQA system. The approach is scalable, accurate and applicable to real-world document processing scenarios.