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Connecting the Real-World and Metaverse via Real-Time Digitization with Intel Arc
DescriptionThe automation process of connecting the real-world is one of the key building blocks for creating a truly immersive metaverse. With the advance of deep learning techniques, we could expect that in future of AR/VR, OCR along with natural language processing (NLP), could help us to digitize, structure or translate all text information according to our needs, from different sources to every AR, VR or mobile wearable device. With the mapping of real-world information to the virtual world, existing or historical knowledge could be best utilized to generate more creative and resourceful results for a more splendid metaverse.
Optical character recognition (OCR) is one of the earliest addressed computer vision tasks, for which different implementations have been proposed. Although in some specific use cases, certain OCR implementations yield very-good results, up to 99% accuracy, a general OCR that could provide high accuracy in different text density, structures of text, fonts, artifacts or text locations is still quite challenging. Therefore, deep-learning based OCR implementation has been widely investigated in recent years, given its strong learning abilities and high robustness. In this hands-on course, what we are learning is 1) a working pipeline to perform automatic digitization based on OCR and NLP algorithms, for uploaded images or through an AR demo; 2) a step-by-step tutorial on how to use OpenVINO to accelerate the inference for OCR and NLP; 3) evaluation and deployment of the solution as an edge-computing system with plain CPU.
Optical character recognition (OCR) is one of the earliest addressed computer vision tasks, for which different implementations have been proposed. Although in some specific use cases, certain OCR implementations yield very-good results, up to 99% accuracy, a general OCR that could provide high accuracy in different text density, structures of text, fonts, artifacts or text locations is still quite challenging. Therefore, deep-learning based OCR implementation has been widely investigated in recent years, given its strong learning abilities and high robustness. In this hands-on course, what we are learning is 1) a working pipeline to perform automatic digitization based on OCR and NLP algorithms, for uploaded images or through an AR demo; 2) a step-by-step tutorial on how to use OpenVINO to accelerate the inference for OCR and NLP; 3) evaluation and deployment of the solution as an edge-computing system with plain CPU.
Event Type
Courses
TimeWednesday, 7 December 20222:00pm - 5:45pm KST
LocationRoom 322, Level 3, West Wing
