Blind quality assessment metric and degradation classification for degraded document images

Shahkolaei, Atena; Beghdadi, Azeddine; Cheriet, Mohamed

doi:10.1016/j.image.2019.04.009

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…Furthermore, we compared DCNet with traditional machine learning, namely support vector machine (SVM) and random forest (RF) [ 7 , 9 ]. We set SVM kernel to RBF function and trained it using one-versus-one approach.…”

Section: Methodsmentioning

confidence: 99%

“…Shahkolei et al had experimented with degradation classification using support vector machines (SVM) [ 8 , 9 ]. As another traditional machine learning technique, here, SVM was used to classify image quality based on a combination of spatial and frequency image features, namely Visual Document Quality Assessment Metrics (VDQAM) and Multi-distortion Document Quality Measure (MDQM).…”

Section: Introductionmentioning

confidence: 99%

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DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient Documents

2021

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Analysis of degraded ancient documents is challenging due to the severity and combination of degradation present in a single image. Ancient documents also suffer from additional noise during the digitalization process, particularly when digitalization is done using low-specification devices and/or under poor illumination conditions. The noises over the degraded ancient documents certainly cause a troublesome document analysis. In this paper, we propose a new noise-robust convolutional neural network (CNN) architecture for degradation classification of noisy ancient documents, which is called a degradation classification network (DCNet). DCNet was constructed based on the ResNet101, MobileNetV2, and ShuffleNet architectures. Furthermore, we propose a new self-transition layer following DCNet. We trained the DCNet using (1) noise-free document images and (2) heavy-noise (zero mean Gaussian noise (ZMGN) and speckle) document images. Then, we tested the resulted models with document images containing different levels of ZMGN and speckle noise. We compared our results to three CNN benchmarking architectures, namely MobileNet, ShuffleNet, and ResNet101. In general, the proposed architecture performed better than MobileNet, ShuffleNet, ResNet101, and conventional machine learning (support vector machine and random forest), particularly for documents with heavy noise.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient Documents

2021

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“…Measuring the objective quality of digital images is a complex process involving a series of low-level and high-level processes in the brain. A plethora of IQA methods have been developed during the last three decades ranging from classical natural images to pictures of ancient manuscript documents and paintings [127]- [130]. One of the most crucial factors used in the design of IQA metrics is visual contrast.…”

Section: A Image Quality Assessment (Iqa)mentioning

confidence: 99%

A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

Beghdadi¹,

Qureshi

Amirshahi

et al. 2020

IEEE Access

Self Cite

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Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the vision research and visual processing scientific community can agree on. This makes it critical to have a clear view on the notion of contrast and its use in various applications. One issue to consider is how to define and particularly use this important measure in developing image processing and analysis methods. In this paper, we present a critical review of contrast measures and associated models developed by the scientific community in vision, optics, and image processing. We also provide learned lessons and guidelines on the use of appropriate contrast measures in selected visual information processing and analysis applications. We discuss challenges and propose research avenues for models enriched by recent findings in the field of human vision research and machine learning. We believe that this work serves as a guideline and can potentially open new research perspectives to the scientific community working on visual information processing and analysis.

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“…A number of blind document image quality assessors (D-IQAs) have been developed to date. Some concentrate on specific degradations such as compression [14] or blur [15,16], while others concentrate on perceptual quality [17][18][19]. More recently, methods have tended towards learning [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

SEDIQA: Sound Emitting Document Image Quality Assessment in a Reading Aid for the Visually Impaired

Courtney

2021

J. Imaging

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For visually impaired people (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in optical character recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue—the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function—no small task for VIPs. In this work, a sound-emitting document image quality assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed no-reference image quality assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images. SEDIQA is found to consistently select the best image for OCR accuracy. The full system includes a document image enhancement technique which introduces improvements in OCR accuracy with an average increase of 22% and a maximum increase of 68%.

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Blind quality assessment metric and degradation classification for degraded document images

Cited by 10 publications

References 33 publications

DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient Documents

DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient Documents

A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

SEDIQA: Sound Emitting Document Image Quality Assessment in a Reading Aid for the Visually Impaired

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