Document Image Quality Assessment Research Articles

Document Image Quality Assessment: A Survey

The rapid emergence of new portable capturing technologies has significantly increased the number and diversity of document images acquired for business and personal applications. The performance of document image processing systems and applications depends directly on the quality of the document images captured. Therefore, estimating the document's image quality is an essential step in the early stages of the document analysis pipeline. This article surveys research on Document Image Quality Assessment (DIQA). We first provide a detailed analysis of both subjective and objective DIQA methods. Subjective methods, including ratings and pair-wise comparison-based approaches, are based on human opinions. Objective methods are based on quantitative measurements, including document modeling and human perception-based methods. Second, we summarize the types and sources of document degradations and techniques used to model degradations. In addition, we thoroughly review two standard measures to characterize document image quality: Optical Character Recognition (OCR)-based and objective human perception-based. Finally, we outline open challenges regarding developing DIQA methods and provide insightful discussion and future research directions for this problem. This survey will become an essential resource for the document analysis research community and serve as a basis for future research.

Quality Assessment Model for Handwritten Photo Document Images

The photo document image's quality determines whether it has the potential to be used for information extraction. Document Image Quality Assessment (DIQA) is a difficult task since it is complicated to train a system to have a complete human-like vision and it might be tiresome and time-consuming to manually evaluate the quality of many document images. The educational system became online during the pandemic age, which led to the digital submission of exams and homework. Quality evaluation has gained more prominence recently as digitization has started to take precedence in some fields. In order to evaluate the document image quality of handwritten document images at the page level while taking into account the overall visual aesthetic, this research suggests a transfer-learning and classification-based model. ResNet50 exceeded all of the pre-trained models we tested and examined, with an accuracy of 91.00 % on validation sets and 83 % on test sets.

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

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%.

Deep-Learning Steganalysis for Removing Document Images on the Basis of Geometric Median Pruning

The deep-learning steganography of current hotspots can conceal an image secret message in a cover image of the same size. While the steganography secret message is primarily removed via active steganalysis. The document image as the secret message in deep-learning steganography can deliver a considerable amount of effective information in a secret communication process. This study builds and implements deep-learning steganography removal models of document image secret messages based on the idea of adversarial perturbation removal: feed-forward denoising convolutional neural networks (DnCNN) and high-level representation guided denoiser (HGD). Further—considering the large computation cost and storage overheads of the above model—we use the document image-quality assessment (DIQA) as threshold, calculate the importance of filters using geometric median and prune redundant filters as extensively as possible through the overall iterative pruning and artificial bee colony (ABC) automatic pruning algorithms to reduce the size of the network structure of the existing vast and over-parameterized deep-learning steganography removal model, while maintaining the good removal effects of the model in the pruning process. Experiment results showed that the model generated by this method has better adaptability and scalability. Compared with the original deep-learning steganography removal model without pruning in this paper, the classic indicators params and flops are reduced by more than 75%.

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

Blind document image quality prediction based on modification of quality aware clustering method integrating a patch selection strategy

Abstract The quality of document images has direct impacts on the performance of document image processing systems. Document Image Quality Assessment (DIQA) is, therefore, of fundamental importance to a numerous document processing applications. As manual quality assessment is almost impossible for a huge volume of document images generated in day-to-day life, it is critical to develop intelligent machine operated methods to estimate the quality of document images. In this paper, a blind document image quality assessment method is proposed to deal with the problem of DIQA in real scenarios, as reference images are not always available. To estimate the quality of a document image, the document is first sampled into a set of patches. The extracted patches are then filtered out based on their level of foreground information using a patch selection strategy. For every selected patch, a cluster assignment is then performed to obtain its quality from a quality aware bag of visual words constructed using k-means clustering. An average pooling is finally employed to estimate the quality of the input document image. To evaluate the proposed method, a dataset composed of document images and three scene image datasets were considered for experimentation. The results obtained from the proposed method demonstrate the effectiveness of the proposed DIQA method. These achievements in applied computational intelligence, expert and decision support systems make a good foundation for creating practical tools to automate document image forgery detection, and archiving process.