公开(公告)号：US12046061B2

公开(公告)日：2024-07-23

申请号：US17364641

申请日：2021-06-30

Applicant: KONICA MINOLTA BUSINESS SOLUTIONS U.S.A., INC.

Inventor： Ting Xu

IPC: G06V30/00 ,  G06F18/211 ,  G06N3/045 ,  G06N3/08 ,  G06V30/32

CPC classification number: G06V30/36 ,  G06F18/211 ,  G06N3/045 ,  G06N3/08

Abstract: A content aware and style aware neural network based data augmentation model generates augmented data sets to train neural network based handwriting recognition models to recognize individuals' handwriting. The augmented data sets may be generated so as to be artificial, and to lack personal or confidential information. The data augmentation model may generate content reference sets of individual characters generated in different fonts, and style reference sets of pluralities of characters of a particular style, for example, an individual's handwriting.

公开(公告)号：US11295155B2

公开(公告)日：2022-04-05

申请号：US16843811

申请日：2020-04-08

Applicant: KONICA MINOLTA BUSINESS SOLUTIONS U.S.A., INC.

Inventor： Ting Xu

IPC: G06K9/62 ,  G06K15/02 ,  G06K9/34 ,  G06T7/11 ,  G06T3/00 ,  G06T5/00 ,  G06T1/20

Abstract: A method and system to generate training data for a deep learning model in memory instead of loading pre-generated data from disk storage. A corpus may be stored as lines of text. The lines of text can be manipulated in the memory of a central processing unit (CPU) of a computing system, using asynchronous multi-processing, in parallel with a training process being conducted on the system's graphics processing unit (GPU). With such an approach, for a given line of text, it is possible to take advantage of different fonts and different types of image augmentation without having to put the images in disk storage for subsequent retrieval. Consequently, the same line of text can be used to generate different training images for use in different epochs, providing more variability in training data (no training sample is trained on more than once). A single training corpus may yield many different training data sets. In one aspect, the model being trained is a deep learning model, which may be one of several different types of neural networks. The training enables the deep learning model to perform OCR on line images.