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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Jiaming Liu

Errui Ding

Junyu Han

Chengquan Zhang

Licheng Tang

Zhibin Hong

Tianshu Hu

Yipeng Sun

Shengyi He

Zuming Huang

Provided are a method for training a font generation model, a method for establishing a font library, and a device. The method for training a font generation model includes the following steps. A source-domain sample character is input into the font generation model to obtain a first target-domain generated character. The first target-domain generated character is input into a font recognition model to obtain the target adversarial loss of the font generation model. The model parameter of the font generation model is updated according to the target adversarial loss.

Method for training a font generation model, method for establishing a font library, and device

A method of generating a font database, and a method of training a neural network model are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology. The method of generating the font database includes: determining, by using a trained similarity comparison model, a basic font database most similar to handwriting font data of a target user in a plurality of basic font databases as a candidate font database; and adjusting, by using a trained basic font database model for generating the candidate font database, the handwriting font data of the target user, so as to obtain a target font database for the target user.

Method of generating font database, and method of training neural network model

The present application discloses a face super-resolution realization method and apparatus, an electronic device and a storage medium, and relate to fields of face image processing and deep learning. The specific implementation solution is as follows: a face part in a first image is extracted; the face part is input into a pre-trained face super-resolution model to obtain a super-sharp face image; a semantic segmentation image corresponding to the super-sharp face image is acquired; and the face part in the first image is replaced with the super-sharp face image, by utilizing the semantic segmentation image, to obtain a face super-resolution image.

Face super-resolution realization method and apparatus, electronic device and storage medium

The present disclosure proposes an end-to-end text recognition method and apparatus, computer device and readable medium. The method comprises: obtaining a to-be-recognized picture containing a text region; recognizing a position of the text region in the to-be-recognized picture and text content included in the text region with a pre-trained end-to-end text recognition model; the end-to-end text recognition model comprising a region of interest perspective transformation processing module for performing perspective transformation processing for the text region. The technical solution of the present disclosure does not need to serially arrange a plurality of steps, and may avoid introducing the accumulated errors and may effectively improve the accuracy of the text recognition.

End-to-end text recognition method and apparatus, computer device and readable medium

A method and apparatus for training a character detector based on weak supervision, a character detection system and a computer readable storage medium are provided, wherein the method includes: inputting coarse-grained annotation information of a to-be-processed object, wherein the coarse-grained annotation information including a whole bounding outline of a word, text bar or line of the to-be-processed object; dividing the whole bounding outline of the coarse-grained annotation information, to obtain a coarse bounding box of a character of the to-be-processed object; obtaining a predicted bounding box of the character of the to-be-processed object through a neural network model from the coarse-grained annotation information; and determining a fine bounding box of the character of the to-be-processed object as character-based annotation of the to-be-processed object, according to the coarse bounding box and the predicted bounding box.

Method and apparatus for training a character detector based on weak supervision, system and medium

A method and apparatus for detecting text are provided. The method includes: extracting features of a to-be-detected image; predicting using a character detection network a probability of each pixel point in the to-be-detected image being a character pixel point, and position information of each pixel point relative to a bounding box of a character including the pixel point when the pixel point is the character pixel point; determining position information of bounding boxes of candidate characters based on the prediction result of the character detection network; inputting the extracted features into a character map network, converting a feature map outputted by the character map network, and generating character vectors; determining a neighboring candidate character of each candidate character, and connecting each candidate character with an associated neighboring candidate character to form a character set; and determining a character area of the to-be-detected image.

Method and apparatus for detecting text

A method and apparatus for training a character detector based on weak supervision, a character detection system and a computer readable storage medium are provided, wherein the method includes: inputting coarse-grained annotation information of a to-be-processed object, wherein the coarse-grained annotation information including a whole bounding outline of a word, text bar or line of the to-be-processed objected; dividing the whole bounding outline of the coarse-grained annotation information, to obtain a coarse bounding box of a character of the to-be-processed object; obtaining a predicted bounding box of the character of the to-be-processed object through a neural network model from the coarse-grained annotation information; and determining a fine bounding box of the character of the to-be-processed object as character-based annotation of the to-be-processed object, according to the coarse bounding box and the predicted bounding box.

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Beijing, China

Jiaming Liu