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...
I tiakina i:
| Hōputu: | Online |
|---|---|
| Reo: | Ingarihi |
| I whakaputaina: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Ngā marau: | |
| Urunga tuihono: | ONIX_20230405_9783036530260_111 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869525629485973504 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-98832 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-988322024-03-28T03:32:25Z Document-Image Related Visual Sensors and Machine Learning Techniques Kyamakya, Kyandoghere Al-Machot, Fadi Mosa, Ahmad Haj Chedjou, Jean Chamberlain 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 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. 2023-04-05T12:53:17Z 2023-04-05T12:53:17Z 2023 book ONIX_20230405_9783036530260_111 9783036530260 9783036530277 https://directory.doabooks.org/handle/20.500.12854/98832 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/6885 https://mdpi.com/books/pdfview/book/6885 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3027-7 10.3390/books978-3-0365-3027-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036530260 9783036530277 166 Basel open access |
| spellingShingle | 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 Document-Image Related Visual Sensors and Machine Learning Techniques |
| title | Document-Image Related Visual Sensors and Machine Learning Techniques |
| title_full | Document-Image Related Visual Sensors and Machine Learning Techniques |
| title_fullStr | Document-Image Related Visual Sensors and Machine Learning Techniques |
| title_full_unstemmed | Document-Image Related Visual Sensors and Machine Learning Techniques |
| title_short | Document-Image Related Visual Sensors and Machine Learning Techniques |
| title_sort | document image related visual sensors and machine learning techniques |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20230405_9783036530260_111 |