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Patent Agency Ranking
公开(公告)号：US20220392243A1
公开(公告)日：2022-12-08
申请号：US17890629
申请日：2022-08-18
Inventor： Shanshan LIU , Meina QIAO , Liang WU , Pengyuan LYU , Sen FAN , Chengquan ZHANG , Kun YAO
Abstract: A method for training a text classification model and an electronic device are provided. The method may include: acquiring a set of to-be-trained images, the set of to-be-trained images including at least one sample image; determining predicted position information and predicted attribute information of each text line in each sample image based on each sample image; and training to obtain the text classification model, based on the annotation position information and the annotation attribute information of each text line in each sample image, and the predicted position information and the predicted attribute information of each text line in each sample image, and the text classification model is used to detect attribute information of each text line in an to-be-recognized image.
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公开(公告)号：US20240304015A1
公开(公告)日：2024-09-12
申请号：US18041265
申请日：2022-04-21
Inventor： Sen FAN , Xiaoyan WANG , Pengyuan LV , Chengquan ZHANG , Kun YAO
IPC: G06V30/19 , G06V30/148 , G06V30/18
CPC classification number: G06V30/19167 , G06V30/153 , G06V30/18 , G06V30/19147 , G06V30/1916
Abstract: The present disclosure provides a method of training a deep learning model for text detection and a text detection method, which relates to the technical field of artificial intelligence, and in particular, to the technical field of computer vision and deep learning and can be used in scenarios of OCR optical character recognition. A method of training a deep learning model for text detection is provided, in which a single character segmentation sub-network outputs a single character segmentation prediction result, a text line segmentation sub-network outputs a text line segmentation prediction result, the trained deep learning model can be used for detecting a text area; and, can at the same time achieve single character segmentation and text line segmentation, and thus is capable to perform text detection by combining two ways of text segmentation, which further improves the accuracy of text area detection.
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公开(公告)号：US20230045715A1
公开(公告)日：2023-02-09
申请号：US17966112
申请日：2022-10-14
Inventor： Chengquan ZHANG , Pengyuan LV , Sen FAN , Kun YAO , Junyu HAN , Jingtuo LIU
Abstract: The present disclosure provides a text detection method, a text recognition method and an apparatus, which relate to the field of artificial intelligence technology, in particular to the field of deep learning and computer vision technologies, and can be applied to scenarios such as optical character recognition. The text detection method is: acquiring an image feature of a text strip in a to-be-recognized image; performing visual enhancement processing on the to-be-recognized image to obtain an enhanced feature map of the to-be-recognized image; comparing the image feature of the text strip with the enhanced feature map for similarity to obtain a target bounding box of the text strip on the enhanced feature map.
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公开(公告)号：US20230186664A1
公开(公告)日：2023-06-15
申请号：US18169032
申请日：2023-02-14
Inventor： Shanshan LIU , Meina QIAO , Liang WU , Pengyuan LV , Sen FAN , Chengquan ZHANG , Kun YAO
CPC classification number: G06V30/19173 , G06V30/19147 , G06V30/30
Abstract: A method for text recognition is disclosed. The method includes obtaining a whole-image scenario for an image to be processed and a text image in the image to be processed. The method further includes determining a first text recognition model corresponding to the whole-image scenario. The method further includes performing text recognition on the text image according to the first text recognition model to obtain text information.
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公开(公告)号：US20230010031A1
公开(公告)日：2023-01-12
申请号：US17946464
申请日：2022-09-16
Inventor： Pengyuan LYU , Sen FAN , Xiaoyan WANG , Yuechen YU , Chengquan ZHANG , Kun YAO , Junyu HAN
Abstract: A method for recognizing a text, an electronic device and a storage medium. An implementation of the method comprises: obtaining a multi-dimensional first feature map of a to-be-recognized image; performing, based on feature values in the first feature map, feature enhancement processing on each feature value in the first feature map; and performing a text recognition on the to-be-recognized image based on the first feature map after the enhancement processing.
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