Document-Image Related Visual Sensors and Machine Learning Techniques

doi:10.3390/books978-3-0365-3027-7

Download Url(s)

https://mdpi.com/books/pdfview/book/6885

Contributor(s)

Kyamakya, Kyandoghere (editor)

Al-Machot, Fadi (editor)

Mosa, Ahmad Haj (editor)

Chedjou, Jean Chamberlain (editor)

Language

English

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Abstract

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.

URI

https://directory.doabooks.org/handle/20.500.12854/98832

Keywords

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