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Document-Image Related Visual Sensors and Machine Learning Techniques

This reprint includes impactful chapters related to document-image related visual sensing, which do present and comprehensively discuss selected scientific concepts, frameworks, architectures and ideas on sensing technologies and machine-learning techniques. Indeed, document imaging/scanning approac...

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Gorde:
Formatua: Online
Hizkuntza:ingelesa
Argitaratua: MDPI - Multidisciplinary Digital Publishing Institute 2023
Gaiak:
feature learning
incomplete multimedia data
fuzzy c-means
variational autoencoder
multispectral imaging
document scanning
portable sensor
depth image filtering
point clouds filtering
Kinect v2
depth resolution
close range
hand pose
image binarization
optical character recognition
document images
local thresholding
image pre-processing
natural images
scene text recognition
visual sensor
text position correction
encoder-decoder network
chart recognition
deep learning
visualization
classification
detection
perspective correction
house architecture type classification
house type classification
convolutional neural networks
document classification
feature selection
data augmentation
imbalanced dataset
scene text detection
multiple scales
n/a
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science
Sarrera elektronikoa:ONIX_20230405_9783036530260_111
Etiketak: Etiketa erantsi
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Gaia:This reprint includes impactful chapters related to document-image related visual sensing, which do present and comprehensively discuss selected scientific concepts, frameworks, architectures and ideas on sensing technologies and machine-learning techniques. Indeed, document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts. This reprint emerging from the Special Issue "Document-Image Related Visual Sensors and Machine Learning Techniques” can be viewed as a result of the crucial need for document management systems. Such technologies are being applied in various fields or different domains and parts of the world to address relevant challenges that could not be addressed without the advances made in these technologies. The reprint includes impactful chapters that present scientific concepts, frameworks, architectures and innovative ideas on sensing technologies and machine-learning techniques to overcome a series of key challenges related to document imaging/scanning, text detection, text recognition, and documents clustering.

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