How to apply Content-based image retrieval for scanned images?
Your problem is studied under the rubric of "image (Quality) assessment" and, more specifically, "document image assessment". Here are links to and abstracts from some relevant surveys:

Document Image Quality Assessment: A Brief Survey


  To maintain, control and enhance the quality of document images and minimize the negative impact of degrada- tions on various analysis and processing systems, it is critical to understand the types and sources of degradations and develop reliable methods for estimating the levels of degradations. This paper provides a brief survey of research on the topic of document image quality assessment. We first present a detailed analysis of the types and sources of document degradations. We then review techniques for document image degradation modeling. Finally, we discuss objective measures and subjective experiments that are used to characterize document image quality.


Subjective and Objective Quality Assessment of Image: A Survey


  With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance. Measuring the image quality is of fundamental importance for numerous image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human quality judgments. Numerous IQA methods have been proposed over the past years to fulfill this goal. In this paper, a survey of the quality assessment methods for conventional image signals, as well as the newly emerged ones, which includes the high dynamic range (HDR) and 3-D images, is presented. A comprehensive explanation of the subjective and objective IQA and their classification is provided. Six widely used subjective quality datasets, and performance measures are reviewed. Emphasis is given to the full-reference image quality assessment (FR-IQA) methods, and 9 often-used quality measures (including mean squared error (MSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), visual information fidelity (VIF), most apparent distortion (MAD), feature similarity measure (FSIM), feature similarity measure for color images (FSIMC), dynamic range independent measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully described, and their performance and computation time on four subjective quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is provided and the issues related to this area of research are reviewed.


No-reference image quality assessment algorithms: A survey


  valuation of noise content or distortions present in an image is same as assessing the quality of an image. Measurement of such quality index is challenging in the absence of reference image. In this paper, a survey of existing algorithms for no-reference image quality assessment is presented. This survey includes type of noise and distortions covered, techniques and parameters used by these algorithms, databases on which the algorithms are validated and benchmarking of their performance with each other and also with human visual system.


Objective image quality assessment: a survey


  Image quality assessment (IQA) is critically important for the image-processing field. IQA aims to build a computational model to predict human perceived image quality, accurately and automatically. Until now, great efforts have been employed to design IQA metrics. In this paper, we systematically and comprehensively review the fundamental, brief history, and state-of-the-art developments of IQA, with emphasis on natural image quality assessment (NIQA). First, the definition of image quality is discussed, which contains three aspects and lead to different philosophies of designing IQA metrics. Afterwards, classic NIQA metrics are presented with some further discussions. Widely used databases and the performances of classic NIQA metrics on them are also listed. We highlight the most significant works and some open issues about the developments of IQA, and provide the benchmarks for the researchers and scholars who work on IQA.




Research papers specifically on document image assessment

Document Image Quality Assessment Using Discriminative Sparse Representation


  The goal of document image quality assessment (DIQA) is to build a computational model which can predict the degree of degradation for document images. Based on the estimated quality scores, the immediate feedback can be provided by document processing and analysis systems, which helps to maintain, organize, recognize and retrieve the information from document images. Recently, the bag-of-visual-words (BoV) based approaches have gained increasing attention from researchers to fulfill the task of quality assessment, but how to use BoV to represent images more accurately is still a challenging problem. In this paper, we propose to utilize a sparse representation based method to estimate document image’s quality with respect to the OCR capability. Unlike the conventional sparse representation approaches, we introduce the target quality scores into the training phase of sparse representation. The proposed method improves the discriminability of the system and ensures the obtained codebook is more suitable for our assessment task. The experimental results on a public dataset show that the proposed method outperforms other hand-crafted and BoV based DIQA approaches.


Discrete Orthogonal Moments Based Framework for Assessing Blurriness of Camera Captured Document Images


  One of the most widely used tasks in the area of image processing is automated processing of documents, which is done using Optical Character Readers (OCR) from document images. The most common form of distortion in document images is blur which can be caused by defocus, motion, camera shake etc. In this paper we propose a no reference image sharpness measure framework using discrete orthogonal moments and image gradients for assessing quality of document images and validated the results against state of the art image sharpness measures and accuracy of three well known Optical Character Readers.


Document Image Quality Assessment Based on Texture Similarity Index


  In this paper, a full reference document image quality assessment (FR DIQA) method using texture features is proposed. Local binary patterns (LBP) as texture features are extracted at the local and global levels for each image. For each extracted LBP feature set, a similarity measure called the LBP similarity index (LBPSI) is computed. A weighting strategy is further proposed to improve the LBPSI obtained based on local LBP features. The LBPSIs computed for both local and global features are then combined to get the final LBPSI, which also provides the best performance for DIQA. To evaluate the proposed method, two different datasets were used. The first dataset is composed of document images, whereas the second one includes natural scene images. The mean human opinion scores (MHOS) were considered as ground truth for performance evaluation. The results obtained from the proposed LBPSI method indicate a significant improvement in automatically/accurately predicting image quality, especially on the document image-based dataset.


No-reference document image quality assessment based on high order image statistics


  Document image quality assessment (DIQA) aims to predict the visual quality of degraded document images. Although the definition of “visual quality” can change based on the specific applications, in this paper, we use OCR accuracy as a metric for quality and develop a novel no-reference DIQA method based on high order image statistics for OCR accuracy prediction. The proposed method consists of three steps. First, normalized local image patches are extracted with regular grid and a comprehensive document image codebook is constructed by K-means clustering. Second, local features are softly assigned to several nearest codewords, and the direct differences between high order statistics of local features and codewords are calculated as global quality aware features. Finally, support vector regression (SVR) is utilized to learn the mapping between extracted image features and OCR accuracies. Experimental results on two document image databases show that the proposed method can accurately predict OCR accuracy and outperforms previous algorithms.


A deep learning approach to document image quality assessment


  This paper proposes a deep learning approach for document image quality assessment. Given a noise corrupted document image, we estimate its quality score as a prediction of OCR accuracy. First the document image is divided into patches and non-informative patches are sifted out using Otsu’s bina- rization technique. Second, quality scores are obtained for all selected patches using a Convolutional Neural Network (CNN), and the patch scores are averaged over the image to obtain the document score. The proposed CNN contains two layers of convolution, location blind max-min pooling, and Rectified Linear Units in the fully connected layers. Exper- iments on two document quality datasets show our method achieved the state of the art performance.


Metric-based no-reference quality assessment of heterogeneous document images


  No-reference image quality assessment (NR-IQA) aims at computing an image quality score that best correlates with either human perceived image quality or an objective quality measure, without any prior knowledge of reference images. Although learning-based NR-IQA methods have achieved the best state-of-the-art results so far, those methods perform well only on the datasets on which they were trained. The datasets usually contain homogeneous documents, whereas in reality, document images come from different sources. It is unrealistic to collect training samples of images from every possible capturing device and every document type. Hence, we argue that a metric-based IQA method is more suitable for heterogeneous documents. We propose a NR-IQA method with the objective quality measure of OCR accuracy. The method combines distortion-specific quality metrics. The final quality score is calculated taking into account the proportions of, and the dependency among different distortions. Experimental results show that the method achieves competitive results with learning-based NR-IQA methods on standard datasets, and performs better on heterogeneous documents.