Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Baidu Online Network Technology (Beijing) Co., Ltd.

Junyu Han

Hao Sun

Errui Ding

Chengquan Zhang

Jingtuo Liu

Xiameng Qin

Yulin Li

Kun Yao

Ju Huang

Yipeng Sun

Haifeng Wang

Jiaming Liu

Qunyi Xie

Tian Wu

Jingdong Wang

Pengyuan Lv

Mengyi En

Zhizhi Guo

Shengxian Zhu

Zuming Huang

Zhibin Hong

Minghao Liu

Xiaoyan Wang

Shanshan Liu

Tianshu Hu

Han Hu

Duohao Qin

Yuanzhang Chang

Yuexiang Hu

Liang Wu

Xiongwei Zhu

Yuxuan Luo

Renyi Zhou

Xiangkai Huang

Qiaoyi Li

Xiangda Yan

Shihu Li

Mengjun Cheng

Longchao Wang

Yangliu Xu

Meina Qiao

Zihan Ni

Ruixue Liu

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Character recognition model training method and apparatus, character recognition method and apparatus, device and storage medium

A pre-training method for a Vision and Scene Text Aggregation model includes: acquiring a sample image-text pair; extracting a sample scene text from a sample image; inputting a sample text into a text encoding network to obtain a sample text feature; inputting the sample image and an initial sample aggregation feature into a visual encoding subnetwork and inputting the initial sample aggregation feature and the sample scene text into a scene encoding subnetwork to obtain a global image feature of the sample image and a learned sample aggregation feature; and pre-training the Vision and Scene Text Aggregation model according to the sample text feature, the global image feature of the sample image, and the learned sample aggregation feature.

Pre-training method, image and text retrieval method for a vision and scene text aggregation model, electronic device, and storage medium

Embodiments of the present disclosure provide a method and apparatus for performing a structured extraction on a text, a device and a storage medium. The method may include: performing a text detection on an entity text image to obtain a position and content of a text line of the entity text image; extracting multivariate information of the text line based on the position and the content of the text line; performing a feature fusion on the multivariate information of the text line to obtain a multimodal fusion feature of the text line; performing category and relationship reasoning based on the multimodal fusion feature of the text line to obtain a category and a relationship probability matrix of the text line; and constructing structured information of the entity text image based on the category and the relationship probability matrix of the text line.

Method and apparatus for performing structured extraction on text, device and storage medium

The disclosure provides a method and a device for processing information, an electronic device, and a storage medium, belonging to a field of artificial intelligence including computer vision, deep learning, and natural language processing. In the method, the computing device recognizes multiple text items in the image. The computing device classifies multiple text items into a first set of name text items and a second set of content text items based on semantics of the text items. The computing device performs a matching operation between the first set and the second set based on a layout of the text items in the image, and determines matched name-content text items. The matched name-content text items include a name text item in the first set and a content text item matching the name text item and in the second set. The computing device outputs the matched name-content text items.

Method and device for processing information, electronic device, and storage medium

A method and apparatus for processing an image, a device and a storage medium are provided. An implementation of the method includes: acquiring a template image, the template image including at least one region of interest; determining a first feature map corresponding to each region of interest in the template image; acquiring a target image; determining a second feature map of the target image; and determining at least one region of interest in the target image according to the first feature map and the second feature map.

Method and apparatus for processing image, device and storage medium

A text recognition method includes: acquiring an image including text information, the text information including M characters, M being a positive integer greater than 1; performing text recognition on the image to acquire character information about the M characters; recognizing reading direction information about each character in accordance with the character information about the M characters, the reading direction information being used to indicate a next character corresponding to a current character in a semantic reading order; and ranking the M characters in accordance with the reading direction information about the M characters to acquire a text recognition result of the text information.

Text recognition method and device, and electronic device

A method, apparatus, device and storage medium for recognizing a bill image may include: performing text detection on a bill image, and determining an attribute information set and a relationship information set of each text box of at least two text boxes in the bill image; determining a type of the text box and an associated text box that has a structural relationship with the text box based on the attribute information set and the relationship information set of the text box; and extracting structured bill data of the bill image, based on the type of the text box and the associated text box that has the structural relationship with the text box.

Method, apparatus, device and storage medium for recognizing bill image

An image processing method, an electronic device and a readable storage medium, which relate to the technical field of computer vision, are disclosed. In an embodiment, a face recognition module and a service processing module maintains respectively an image buffer queue; the face recognition module maintains a face image buffer queue of face images, and the service processing module maintains a background image buffer queue of background images, i.e., first images; since the face recognition module only maintains the face image buffer queue, only a determined optimal face image, i.e., a face image to be matched, is transmitted to the service processing module, and then, the service processing module determines a background image matched with the optimal face image transmitted by the face recognition module from the maintained background image buffer queue, thus performing image recognition and image matching on a face appearing in a video source.

Image processing method, electronic device and readable storage medium for maintaining a face image buffer queue

A method for visual question answering, a computer device implementing the method and a medium for storing instructions on performing the method are provided. The method includes: acquiring an input image and an input question; constructing a visual graph based on the input image, wherein the visual graph comprises a first node feature and a first edge feature; constructing a question graph based on the input question, wherein the question graph comprises a second node feature and a second edge feature; performing a multimodal fusion on the visual graph and the question graph to obtain an updated visual graph and an updated question graph; determining a question feature based on the input question; determining a fusion feature based on the updated visual graph, the updated question graph and the question feature; and generating a predicted answer for the input image and the input question.

Method and apparatus for visual question answering, computer device and medium

The present disclosure provides a method for visual question answering, which relates to a field of computer vision and natural language processing. The method includes: acquiring an input image and an input question; constructing a Visual Graph based on the input image, wherein the Visual Graph comprises a Node Feature and an Edge Feature; updating the Node Feature by using the Node Feature and the Edge Feature to obtain an updated Visual Graph; determining a question feature based on the input question; fusing the updated Visual Graph and the question feature to obtain a fused feature; and generating a predicted answer for the input image and the input question based on the fused feature. The present disclosure further provides an apparatus for visual question answering, a computer device and a non-transitory computer-readable storage medium.

Method and device for visual question answering, computer apparatus and medium

The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.

Image recognition method, apparatus, device, and computer storage medium

Embodiments of the present disclosure provide a method and apparatus for correcting a distorted document image, where the method for correcting a distorted document image includes: obtaining a distorted document image; and inputting the distorted document image into a correction model, and obtaining a corrected image corresponding to the distorted document image; where the correction model is a model obtained by training with a set of image samples as inputs and a corrected image corresponding to each image sample in the set of image samples as an output, and the image samples are distorted. By inputting the distorted document image to be corrected into the correction model, the corrected image corresponding to the distorted document image can be obtained through the correction model, which realizes document image correction end-to-end, improves accuracy of the document image correction, and extends application scenarios of the document image correction.

Method and apparatus for correcting distorted document image

An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

Image recognition method and apparatus, device, and computer storage medium

Disclosed are a method, apparatus and electronic device for annotating information of a structured document. A specific implementation is: obtaining a template image of a structured document and at least one piece of annotation information of a field to be filled in the template image, where the annotation information includes attribute value and historical content of the field to be filled, and historical position of the field to be filled in the template image; generating, according to the attribute value of the field to be filled, the historical content of the field to be filled and the historical position of the field to be filled in the template image, target filling information of the field to be filled; obtaining, according to the target filling information of the field to be filled, an image of an annotated structured document.

Method, apparatus and electronic device for annotating information of structured document

A method and apparatus for detecting text regions in an image, a device, and a medium are provided. The method may include: detecting, based on feature representation of an image, a first text region in the image, where the first text region covers a text in the image, a region occupied by the text being of a certain shape; determining, based on a feature block of the first text region, text geometry information associated with the text, where the text geometry information includes a text centerline of the text and distance information of the centerline from the upper and lower borders of the text; and adjusting, based on the text geometry information associated with the text, the first text region to a second text region, where the second text region also covers the text and is smaller than the first text region.

Method and apparatus for detecting text regions in image, device, and medium

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Beijing, China

Junyu Han