Computer Vision: Python OCR & Object Detection Quick Starter
Get to grips with optical character recognition, image recognition, object detection, and object recognition using Python About This Video Understand the optical character recognition (OCR) technology Explore convolutional neural networks pre-trained models for image recognition Use Mask R-CNN pre-t...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Körperschaft: | |
| Format: | Elektronisch Video |
| Sprache: | Englisch |
| Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
Packt Publishing
2020
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| Ausgabe: | 1st edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781800567481/?ar |
| Zusammenfassung: | Get to grips with optical character recognition, image recognition, object detection, and object recognition using Python About This Video Understand the optical character recognition (OCR) technology Explore convolutional neural networks pre-trained models for image recognition Use Mask R-CNN pre-trained models and MobileNet-SSD for object detection In Detail This course is a quick starter for anyone who wants to explore optical character recognition (OCR), image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process. Starting with an introduction to the OCR technology, you'll get your system ready for Python coding by installing Anaconda packages and the necessary libraries and dependencies. As you advance, you'll work with convolutional neural networks (CNNs), the Keras library, and pre-trained models, such as VGGNet 16 and VGGNet 19, for performing image recognition with the help of sample images. The course then focuses on object recognition and shows you how to use MobileNet-SSD and Mask R-CNN pre-trained models to detect and label objects in a real-time live video from the computer's webcam as well as in a saved video. Toward the end, you'll learn how the YOLO model and the lite version, Tiny YOLO, fasten the process of detecting an object from a single image. By the end of the course, you'll have developed a solid understanding of OCR and the methods involved and gain the confidence to perform optical character recognition using Python with ease. |
| Beschreibung: | Not recommended for use on the libraries' public computers |
| Umfang: | 1 Online-Ressource (1 streaming video file, approximately 4 hr., 32 min.) |
| ISBN: | 1800567480 9781800567481 |
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| 520 | |a Get to grips with optical character recognition, image recognition, object detection, and object recognition using Python About This Video Understand the optical character recognition (OCR) technology Explore convolutional neural networks pre-trained models for image recognition Use Mask R-CNN pre-trained models and MobileNet-SSD for object detection In Detail This course is a quick starter for anyone who wants to explore optical character recognition (OCR), image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process. Starting with an introduction to the OCR technology, you'll get your system ready for Python coding by installing Anaconda packages and the necessary libraries and dependencies. As you advance, you'll work with convolutional neural networks (CNNs), the Keras library, and pre-trained models, such as VGGNet 16 and VGGNet 19, for performing image recognition with the help of sample images. The course then focuses on object recognition and shows you how to use MobileNet-SSD and Mask R-CNN pre-trained models to detect and label objects in a real-time live video from the computer's webcam as well as in a saved video. Toward the end, you'll learn how the YOLO model and the lite version, Tiny YOLO, fasten the process of detecting an object from a single image. By the end of the course, you'll have developed a solid understanding of OCR and the methods involved and gain the confidence to perform optical character recognition using Python with ease. | ||
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| spelling | Nelson, Abhilash VerfasserIn aut Computer Vision Python OCR & Object Detection Quick Starter Nelson, Abhilash 1st edition. [Erscheinungsort nicht ermittelbar] Packt Publishing 2020 1 Online-Ressource (1 streaming video file, approximately 4 hr., 32 min.) zweidimensionales bewegtes Bild tdi rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Not recommended for use on the libraries' public computers Get to grips with optical character recognition, image recognition, object detection, and object recognition using Python About This Video Understand the optical character recognition (OCR) technology Explore convolutional neural networks pre-trained models for image recognition Use Mask R-CNN pre-trained models and MobileNet-SSD for object detection In Detail This course is a quick starter for anyone who wants to explore optical character recognition (OCR), image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process. Starting with an introduction to the OCR technology, you'll get your system ready for Python coding by installing Anaconda packages and the necessary libraries and dependencies. As you advance, you'll work with convolutional neural networks (CNNs), the Keras library, and pre-trained models, such as VGGNet 16 and VGGNet 19, for performing image recognition with the help of sample images. The course then focuses on object recognition and shows you how to use MobileNet-SSD and Mask R-CNN pre-trained models to detect and label objects in a real-time live video from the computer's webcam as well as in a saved video. Toward the end, you'll learn how the YOLO model and the lite version, Tiny YOLO, fasten the process of detecting an object from a single image. By the end of the course, you'll have developed a solid understanding of OCR and the methods involved and gain the confidence to perform optical character recognition using Python with ease. Internet videos Streaming video Vidéos sur Internet Vidéo en continu streaming video Electronic videos Safari, an O'Reilly Media Company. MitwirkendeR ctb |
| spellingShingle | Nelson, Abhilash Computer Vision Python OCR & Object Detection Quick Starter Internet videos Streaming video Vidéos sur Internet Vidéo en continu streaming video Electronic videos |
| title | Computer Vision Python OCR & Object Detection Quick Starter |
| title_auth | Computer Vision Python OCR & Object Detection Quick Starter |
| title_exact_search | Computer Vision Python OCR & Object Detection Quick Starter |
| title_full | Computer Vision Python OCR & Object Detection Quick Starter Nelson, Abhilash |
| title_fullStr | Computer Vision Python OCR & Object Detection Quick Starter Nelson, Abhilash |
| title_full_unstemmed | Computer Vision Python OCR & Object Detection Quick Starter Nelson, Abhilash |
| title_short | Computer Vision |
| title_sort | computer vision python ocr object detection quick starter |
| title_sub | Python OCR & Object Detection Quick Starter |
| topic | Internet videos Streaming video Vidéos sur Internet Vidéo en continu streaming video Electronic videos |
| topic_facet | Internet videos Streaming video Vidéos sur Internet Vidéo en continu streaming video Electronic videos |
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