View source for Capital one services, llc (20240265258). TRAINING A NEURAL NETWORK MODEL FOR RECOGNIZING HANDWRITTEN SIGNATURES BASED ON DIFFERENT CURSIVE FONTS AND TRANSFORMATIONS simplified abstract

=TRAINING A NEURAL NETWORK MODEL FOR RECOGNIZING HANDWRITTEN SIGNATURES BASED ON DIFFERENT CURSIVE FONTS AND TRANSFORMATIONS=


==Organization Name==

[[:Category:capital one services, llc|capital one services, llc]]


[[Category:capital one services, llc]]

==Inventor(s)==

[[:Category:Reza Farivar of Champaign IL (US)|Reza Farivar of Champaign IL (US)]][[Category:Reza Farivar of Champaign IL (US)]]

[[:Category:Fardin Abdi Taghi Abad of Champaign IL (US)|Fardin Abdi Taghi Abad of Champaign IL (US)]][[Category:Fardin Abdi Taghi Abad of Champaign IL (US)]]

[[:Category:Anh Truong of Champaign IL (US)|Anh Truong of Champaign IL (US)]][[Category:Anh Truong of Champaign IL (US)]]

[[:Category:Mark Watson of Urbana IL (US)|Mark Watson of Urbana IL (US)]][[Category:Mark Watson of Urbana IL (US)]]

[[:Category:Austin Walters of Savoy IL (US)|Austin Walters of Savoy IL (US)]][[Category:Austin Walters of Savoy IL (US)]]

[[:Category:Jeremy Goodsitt of Champaign IL (US)|Jeremy Goodsitt of Champaign IL (US)]][[Category:Jeremy Goodsitt of Champaign IL (US)]]

[[:Category:Vincent Pham of Champaign IL (US)|Vincent Pham of Champaign IL (US)]][[Category:Vincent Pham of Champaign IL (US)]]

==TRAINING A NEURAL NETWORK MODEL FOR RECOGNIZING HANDWRITTEN SIGNATURES BASED ON DIFFERENT CURSIVE FONTS AND TRANSFORMATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240265258 titled 'TRAINING A NEURAL NETWORK MODEL FOR RECOGNIZING HANDWRITTEN SIGNATURES BASED ON DIFFERENT CURSIVE FONTS AND TRANSFORMATIONS

'''Simplified Explanation''': This patent application describes a device that generates images of different cursive first names and last names, combines them to create signature images, and trains a neural network model to recognize signatures.

'''Key Features and Innovation''':
* Device applies different cursive fonts to first names and last names.
* Different transformations are applied to the images to generate sets of first name and last name images.
* Neural network model is trained with signature images to recognize first names and last names in signatures.

'''Potential Applications''':
This technology can be used in document verification, signature recognition systems, and personalized marketing materials.

'''Problems Solved''':
This technology addresses the need for accurate signature recognition and personalized content generation.

'''Benefits''':
* Improved accuracy in signature recognition.
* Enhanced personalization in marketing materials.
* Efficient document verification processes.

'''Commercial Applications''':
Title: Signature Recognition Technology for Document Verification and Personalized Marketing
This technology can be utilized in banks, legal firms, marketing agencies, and any organization requiring signature verification and personalized content creation.

'''Prior Art''''':
Researchers can explore prior art related to signature recognition systems, cursive font applications, and neural network models for signature analysis.

'''Frequently Updated Research''':
Researchers can stay updated on advancements in neural network models for signature recognition and cursive font generation techniques.

'''Questions about Signature Recognition Technology''':
1. How does this technology improve document verification processes?
2. What are the potential limitations of using neural network models for signature recognition?


==Original Abstract Submitted==

a device receives information indicating first names and last names of individuals and applies different cursive fonts to each of the first names and the last names to generate images of different cursive first names and different cursive last names. the device applies different transformations to the images of the different cursive first names and the different cursive last names to generate a set of first name images and a set of last name images. the device combines each first name image with each last name image to form a set of signature images and trains a neural network model, with the set of signature images, to generate a trained neural network model. the device receives an image of a signature and processes the image of the signature, with the trained neural network model, to recognize a first name and a last name in the signature.



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