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公开(公告)号：US20240281609A1
公开(公告)日：2024-08-22
申请号：US18041207
申请日：2022-05-16
Inventor： Pengyuan LV , Jingquan LI , Chengquan ZHANG , Kun YAO , Jingtuo LIU , Junyu HAN
Abstract: The present application provides a method of training a text recognition model. The method includes: inputting a first sample image into the visual feature extraction sub-model to obtain a first visual feature and a first predicted text, the first sample image contains a text and a tag indicating a first actual text; obtaining, by using the semantic feature extraction sub-model, a first semantic feature based on the first predicted text; obtaining, by using the sequence sub-model, a second predicted text based on the first visual feature and the first semantic feature; and training the text recognition model based on the first predicted text, the second predicted text and the first actual text. The present disclosure further provides a method of recognizing a text, an electronic device, and a storage medium.
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公开(公告)号：US20230401828A1
公开(公告)日：2023-12-14
申请号：US17905965
申请日：2022-04-08
Inventor： Meina QIAO , Shanshan LIU , Xiameng QIN , Chengquan ZHANG , Kun YAO
IPC: G06V10/774 , G06V30/14 , G06V10/764
CPC classification number: G06V10/774 , G06V10/764 , G06V30/1444
Abstract: A method for training an image recognition model includes: obtaining a training data set, in which the training data set includes first text images of each vertical category in a non-target scene and second text images of each vertical category in a target scene, and a type of text content involved in the first text images is the same as a type of text content involved in the second text image; training an initial recognition model by using the first text images, to obtain a basic recognition model; and modifying the basic recognition model by using the second text images, to obtain an image recognition model corresponding to the target scene.
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公开(公告)号：US20230124389A1
公开(公告)日：2023-04-20
申请号：US17887690
申请日：2022-08-15
Inventor： Longchao WANG , Yipeng SUN , Kun YAO , Junyu HAN , Jingtuo LIU , Errui DING
IPC: G06V10/70 , G06V10/774
Abstract: A model determination method and electronic device is provided, and relates to the technical field of artificial intelligence and, in particular, to the field of computer visions and deep learning, and can be applied to image processing, image identification and other scenarios. A specific implementation solution includes an image sample and a text sample are acquired, wherein text data in the text sample is used for performing text description to target image data in the image sample; at least one image feature in the image sample is stored to a first queue, and at least text feature in the text sample is stored to a second queue; the first queue and the second queue are trained to obtain a first target model; and the first target model is determined as an initialization model for a second target model.
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公开(公告)号：US20220292131A1
公开(公告)日：2022-09-15
申请号：US17826760
申请日：2022-05-27
Inventor： Ruibin BAI , Xiang WEI , Yipeng SUN , Kun YAO , Jingtuo LIU , Junyu HAN
IPC: G06F16/583 , G06V10/74 , G06V10/44 , G06F16/535
Abstract: A method, apparatus and system for retrieving an image is provided, the method comprises: detecting, in response to receiving a query request comprising a target image, a target subject from the target image; extracting a subject feature from the target subject if a confidence level of a detection box of the detected target subject is greater than a first threshold, the subject feature comprising an identical feature, a similar feature and a category; performing matching on the subject feature of the target image and a subject feature of a candidate image pre-stored in a database, to obtain a similarity score and an identicalness score of the candidate image; and selecting, according to the similarity score and the identicalness score, a predetermined number of candidate images as a search result for output.
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公开(公告)号：US20230048495A1
公开(公告)日：2023-02-16
申请号：US17974183
申请日：2022-10-26
Inventor： Qunyi XIE , Xiameng QIN , Mengyi EN , Dongdong ZHANG , Ju HUANG , Yangliu XU , Yi CHEN , Kun YAO
IPC: G06V30/413 , G06V10/764 , G06V10/24 , G06V10/75 , G06V30/414
Abstract: A method and a platform of generating a document, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a text recognition scenario and other scenarios. The method includes: performing a category recognition on a document picture to obtain a target category result; determining a target structured model matched with the target category result; and performing, by using the target structured model, a structure recognition on the document picture to obtain a structure recognition result, so as to generate an electronic document based on the structure recognition result, wherein the structure recognition result includes a field attribute recognition result and a field position recognition result.
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Patent Agency Ranking
公开(公告)号：US20230020022A1
公开(公告)日：2023-01-19
申请号：US17885882
申请日：2022-08-11
Inventor： Shanshan LIU , Meina QIAO , Liang WU , Chengquan ZHANG , Kun YAO
Abstract: A method of recognizing a text, which relates to a field of an artificial intelligence technology, in particular to a field of computer vision and deep learning technology, and may be applied to optical character recognition or other applications. The method includes: acquiring a plurality of image sequences by continuously scanning a document; performing an image stitching, so as to obtain a plurality of successive frames of stitched images corresponding to the plurality of image sequences respectively, an overlapping region exists between each two successive frames of stitched images; performing a text recognition based on the plurality of successive frames of stitched images, so as to obtain a plurality of corresponding recognition results; and performing a de-duplication on the plurality of recognition results based on the overlapping region between each two successive frames of stitched images, so as to obtain a text recognition result for the document.
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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：US20220392242A1
公开(公告)日：2022-12-08
申请号：US17819838
申请日：2022-08-15
Abstract: A method for training a text positioning model includes: obtaining a sample image, where the sample image contains a sample text to be positioned and a text marking box for the sample text; inputting the sample image into a text positioning model to be trained to position the sample text, and outputting a prediction text box for the sample image; obtaining a sample prior anchor box corresponding to the sample image; and adjusting model parameters of the text positioning model based on the sample prior anchor box, the text marking box and the prediction text box, and continuing training the adjusted text positioning model based on a next sample image until model training is completed, to generate a target text positioning model.
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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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10.发明公开公开(公告)号：US20230196805A1
公开(公告)日：2023-06-22
申请号：US18168089
申请日：2023-02-13
Inventor： Ju HUANG , Xiaoqiang ZHANG , Xiameng QIN , Chengquan ZHANG , Kun YAO
Abstract: The present disclosure provides a character detection method and apparatus, a model training method and apparatus, a device and a storage medium. The specific implementation is: acquiring a training sample, where the training sample includes a sample image and a marked image, and the marked image is an image obtained by marking a text instance in the sample image; inputting the sample image into a character detection model, to obtain segmented images and image types of the segmented images output by the character detection model, where the image type indicates that the segmented image includes a text instance, or the segmented image does not include a text instance; and adjusting a parameter of the character detection model according to the segmented images, the image types of the segmented images and the marked image.
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