Snap inc. (20240338900). OPTICAL CHARACTER RECOGNITION FOR AUGMENTED IMAGES simplified abstract
Contents
OPTICAL CHARACTER RECOGNITION FOR AUGMENTED IMAGES
Organization Name
Inventor(s)
Kevin Sarabia Dela Rosa of Seattle WA (US)
Byung Eun Jeon of Seattle WA (US)
OPTICAL CHARACTER RECOGNITION FOR AUGMENTED IMAGES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338900 titled 'OPTICAL CHARACTER RECOGNITION FOR AUGMENTED IMAGES
Simplified Explanation: The patent application describes a system for performing optical character recognition on augmented images, where a machine learning model is used to recognize graphical elements in real-world environments.
- The system accesses an image showing a real-world environment with a graphical element added.
- By applying a machine learning model, the system identifies and recognizes the graphical element in the image.
- A standard code representing the recognized graphical element is stored in association with the image.
Key Features and Innovation:
- Optical character recognition on augmented images.
- Machine learning model used for recognizing graphical elements.
- Storage of standard codes for recognized graphical elements.
Potential Applications: This technology can be used in augmented reality applications, image processing systems, and document scanning tools.
Problems Solved: The system addresses the challenge of recognizing and processing graphical elements in augmented images accurately and efficiently.
Benefits:
- Improved accuracy in recognizing graphical elements.
- Enhanced efficiency in processing augmented images.
- Standardization of codes for graphical elements.
Commercial Applications: Potential commercial applications include augmented reality apps, image editing software, and document management systems.
Questions about Optical Character Recognition on Augmented Images: 1. How does the machine learning model improve the accuracy of recognizing graphical elements in augmented images? 2. What are the potential challenges in implementing this technology in real-world applications?
Frequently Updated Research: Stay updated on advancements in machine learning models for optical character recognition and image processing techniques.
Original Abstract Submitted
methods and systems are disclosed for performing optical character recognition on augmented images. the system accesses an image depicting a real-world environment augmented with a graphical element. the system recognizes the graphical element in the image by applying a machine learning (ml) model to the image. the system stores a standard code representing the graphical element that has been recognized in association with the image.
