Document-Image Related Visual Sensors and Machine Learning Techniques

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Physics; Research and information: general; chart recognition; classification; close range; convolutional neural networks; data augmentation; deep learning; depth image filtering; depth resolution; detection; document classification; document images; document scanning; encoder-decoder network; feature learning; feature selection; fuzzy c-means; hand pose; house architecture type classification; house type classification; image binarization; image pre-processing; imbalanced dataset; incomplete multimedia data; Kinect v2; local thresholding; multiple scales; multispectral imaging; n/a; natural images; optical character recognition; perspective correction; point clouds filtering; portable sensor; scene text detection; scene text recognition; text position correction; variational autoencoder; visual sensor; visualization
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication

 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.