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

Courtney, Jane

doi:10.3390/jimaging7090168

Cited by 3 publications

(1 citation statement)

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“…In the following sections, we have given a brief description in regard to the mentioned no reference image quality evaluators. (You and Korhonen, 2022;Leveque et al, 2021;Qi et al, 2021;Gueraichi and Serir, 2020;Kinoshita and Kiya, 2019;Courtney, 2021;Fu et al, 2021;Chan and Goldsmith, 2002).…”

Section: Visual Quality Assessment Of the Dataset Samplesmentioning

confidence: 99%

Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network

S¹,

Martis²,

Nayak³

et al. 2023

International Journal of E-Health and Medical Communications

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Treatment of influenza and its complications is a major challenge for healthcare systems. Pyrazine is one drug used in treating influenza. Aspergillic acid is major antibiotic constituent in pyrazine compounds mined from Aspergillus flavus' final stage. This stage of flavus is detected through color change forming a pale-yellow crystal structure. Detection of the same is complex and demands an experienced fraternity to continuously monitor the growth of fungus and identify its color change. However, researches proved that the task needs to be perfect and a tiny human error leads to a catastrophe in antibiotic creation. To avoid these flaws, druggists make a huge investment on costly equipment for accurate detection. To overcome these drawbacks, this article proposes a hybrid quantum convolutional neural network that predicts various stages of the fungus from the microscope's sample. To train the network, about 47,000 samples were poised under typical lab settings. The proposed system was tested in usual conditions and positively isolated the mature samples with 96% efficiency.

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Section: Visual Quality Assessment Of the Dataset Samplesmentioning

confidence: 99%

Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network

S¹,

Martis²,

Nayak³

et al. 2023

International Journal of E-Health and Medical Communications

View full text Add to dashboard Cite

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Document Image Quality Assessment: A Survey

et al. 2023

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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 paper 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.

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