View source for International business machines corporation (20240096121). TRAINING AND USING A VECTOR ENCODER TO DETERMINE VECTORS FOR SUB-IMAGES OF TEXT IN AN IMAGE SUBJECT TO OPTICAL CHARACTER RECOGNITION simplified abstract

=TRAINING AND USING A VECTOR ENCODER TO DETERMINE VECTORS FOR SUB-IMAGES OF TEXT IN AN IMAGE SUBJECT TO OPTICAL CHARACTER RECOGNITION=


==Organization Name==

[[:Category:international business machines corporation|international business machines corporation]]


[[Category:international business machines corporation]]

==Inventor(s)==

[[:Category:Zhong Fang Yuan of Xi'an (CN)|Zhong Fang Yuan of Xi'an (CN)]][[Category:Zhong Fang Yuan of Xi'an (CN)]]

[[:Category:Tong Liu of Xi'an (CN)|Tong Liu of Xi'an (CN)]][[Category:Tong Liu of Xi'an (CN)]]

[[:Category:Yi Chen Zhong of Shanghai (CN)|Yi Chen Zhong of Shanghai (CN)]][[Category:Yi Chen Zhong of Shanghai (CN)]]

[[:Category:Xiang Yu Yang of Xi'an (CN)|Xiang Yu Yang of Xi'an (CN)]][[Category:Xiang Yu Yang of Xi'an (CN)]]

[[:Category:Guan Chao Li of Shanghai (CN)|Guan Chao Li of Shanghai (CN)]][[Category:Guan Chao Li of Shanghai (CN)]]

==TRAINING AND USING A VECTOR ENCODER TO DETERMINE VECTORS FOR SUB-IMAGES OF TEXT IN AN IMAGE SUBJECT TO OPTICAL CHARACTER RECOGNITION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240096121 titled 'TRAINING AND USING A VECTOR ENCODER TO DETERMINE VECTORS FOR SUB-IMAGES OF TEXT IN AN IMAGE SUBJECT TO OPTICAL CHARACTER RECOGNITION

==Simplified Explanation==

The abstract describes a computer program product, system, and method for training and using a vector encoder to determine vectors for sub-images of text in an image to subject to optical character recognition. The vector encoder is trained to encode images representing text into vectors in a vector space, where vectors of images representing similar text have a high degree of cohesion in the vector space, and vectors of images representing dissimilar text have a low degree of cohesion in the vector space. The input image is processed to determine sub-images that bound text, which are then inputted to the vector encoder to output sub-image vectors. A search vector is generated for search text, and optical character recognition is applied to at least one region of the input image including the sub-images with sub-image vectors matching the search vector.

* Trained vector encoder encodes images of text into vectors in a vector space.
* Vectors of images representing similar text have high cohesion in the vector space.
* Vectors of images representing dissimilar text have low cohesion in the vector space.
* Input image is processed to determine sub-images containing text.
* Sub-images are inputted to the vector encoder to output sub-image vectors.
* Search vector is generated for search text.
* Optical character recognition is applied to regions of the input image with matching sub-image vectors.

== Potential Applications ==
This technology can be applied in document scanning, text recognition in images, and automated data extraction from images.

== Problems Solved ==
This technology solves the problem of accurately identifying and extracting text from images, especially in scenarios where traditional OCR methods may struggle due to variations in text appearance.

== Benefits ==
The benefits of this technology include improved accuracy in text recognition, faster data extraction from images, and enhanced automation in document processing tasks.

== Potential Commercial Applications ==
Potential commercial applications of this technology include document management systems, automated data entry software, and image-based text search engines.

== Possible Prior Art ==
One possible prior art is the use of convolutional neural networks for image recognition and text extraction, but this technology specifically focuses on training a vector encoder for text images and applying OCR based on vector similarity.

=== Unanswered Questions ===

=== How does this technology compare to existing OCR methods in terms of accuracy and efficiency? ===
This article does not provide a direct comparison between this technology and existing OCR methods. 

=== What are the potential limitations or challenges in implementing this technology on a large scale? ===
The article does not address the potential limitations or challenges in implementing this technology on a large scale.


==Original Abstract Submitted==

provided are a computer program product, system, and method for training and using a vector encoder to determine vectors for sub-images of text in an image to subject to optical character recognition. a vector encoder is trained to encode images representing text into vectors in a vector space. vectors of images representing similar text have a high degree of cohesion in the vector space. vectors of images representing dissimilar text have a low degree of cohesion in the vector space. an input image is processed to determine sub-images of the input image that bound text represented in the input image. the sub-images are inputted to the vector encoder to output sub-image vectors. the vector encoder generates a search vector for search text. optical character recognition is applied to at least one region of the input image including the sub-images having sub-image vectors matching the search vector.



[[Category:G06V30/148]]
[[Category:G06V10/774]]
[[Category:G06V10/82]]
[[Category:G06V30/182]]