Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
Type:
Grant
Filed:
October 20, 2022
Date of Patent:
December 26, 2023
Assignee:
Hyper Labs, Inc.
Inventors:
Stefan Iliev Stefanov, Boris Nikolaev Daskalov, Akhil Lohchab
Abstract: In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.
Type:
Grant
Filed:
June 1, 2020
Date of Patent:
March 21, 2023
Assignee:
Hyper Labs, Inc.
Inventors:
Boris Nikolaev Daskalov, Daniel Biser Balchev
Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
Type:
Application
Filed:
October 20, 2022
Publication date:
February 16, 2023
Applicant:
Hyper Labs, Inc.
Inventors:
Stefan Iliev STEFANOV, Boris Nikolaev DASKALOV, Akhil LOHCHAB
Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
Type:
Grant
Filed:
January 16, 2020
Date of Patent:
October 25, 2022
Assignee:
Hyper Labs, Inc.
Inventors:
Stefan Iliev Stefanov, Boris Nikolaev Daskalov, Akhil Lohchab
Abstract: In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.
Type:
Grant
Filed:
November 5, 2019
Date of Patent:
June 2, 2020
Assignee:
Hyper Labs, Inc.
Inventors:
Boris Nikolaev Daskalov, Daniel Biser Balchev