Boe Technology Group Co., Ltd.

Guangwei Huang

Yue Li

Bingchuan Shi

Jibo Zhao

Ruibin Xue

Xiaojun Tang

Tingting Wang

Huanhuan Zhang

Rui Zheng

Zhanfu An

Provided is a method for recognizing a text. The method includes: acquiring a text image; determining at least one text box in the text image, wherein each of the at least one text box corresponds to at least one word; determining, from the at least one text box, a text box to be recognized; determining a picture unit corresponding to the text box to be recognized in the text image; rotating the picture unit to a target posture; and determining a target recognition result by performing text recognition on the picture unit in the target posture.

Method and apparatus for recognizing text, storage medium, and electronic device

A method for training an object detection model includes: firstly obtaining M sample image sets; then, obtaining an initial object detection model; and finally, training the initial object detection model by using the M sample image sets to obtain the object detection model. A sample image set includes at least one sample image and object type(s) of object(s) in each sample image. An object type corresponds to one sample image set, and the M sample image sets correspond to N object types.

Method for training object detection model, object detection method and apparatus

A method and apparatus for recognizing an imaged information-bearing medium, a computer-readable storage device and a computer device are provided. The method comprising: acquiring a first image of the imaged information-bearing medium; performing text recognition on the first image to acquire a text content of the imaged information-bearing medium; classifying the imaged information-bearing medium to acquire a type of the imaged information-bearing medium; and archiving the text content according to the type.

Method and apparatus for recognizing imaged information-bearing medium, computer device and medium

A character recognition method includes: performing feature extraction on an image to be recognized to obtain a first feature map; processing the first feature map to at least obtain N first candidate carrier detection boxes, each first candidate carrier detection box being configured to outline a region of a character carrier; screening the N first candidate carrier detection boxes to obtain K first target carrier detection boxes; performing a feature extraction on the first feature map to obtain a second feature map; processing the second feature map to obtain L first candidate character detection boxes, each first candidate character detection box being configured to outline a region containing at least one character; screening the L first candidate character detection boxes to obtain J first target character detection boxes; and recognizing characters in the J first target character detection boxes to obtain J target character informations.

Character recognition method and terminal device

The present disclosure relates to a handwritten text recognition method, including: acquiring an information sequence including a plurality of track points of handwritten text, wherein information on each track point comprises its abscissa, writing time and writing state value; dividing the plurality of track points into a plurality of strokes according to the writing state value of each track point, the writing state value including a first value representative of stroke pen-up and a second value representative of stroke pen-down, respectively; calculating a first segmentation threshold of the handwritten text; determining a first text segmentation point according to a result of comparison between an absolute value of a difference between abscissas of a start track point of one stroke and an end track point of its previous stroke and the first segmentation threshold; and performing text segmentation according to the first text segmentation point to obtain a text segmentation result.

Handwritten text recognition method, apparatus and system, handwritten text search method and system, and computer-readable storage medium

The present disclosure provides an answer evaluation method, an answer evaluation system, an electronic device, and a medium. The method comprises: acquiring an answer image, for a test paper answered by a use; classifying the answer image based on a pre-trained test question classification model, so as to obtain an objective question answer area and a subjective question answer area; identifying at least one objective question in the objective question answer area and an objective question answers for each of the at least one objective question; identifying at least one subjective question in the subjective question answer area and a subjective question answers for each of the at least one subjective question; and determining a total score value of the test paper based on the objective question, the objective question answer, the subjective question and the subjective question answer.

Answer evaluation method, answer evaluation system, electronic device, and medium

The present invention provides a word recognition method. The method includes: acquiring an image of a word to be recognized; recognizing edges of each character of the word to be recognized from the image of the word to be recognized; determining a geometric position of the word to be recognized; stretching the geometric position of the word to be recognized to a horizontal position; and recognizing the word to be recognized in the horizontal position.

Word recognition method, apparatus and storage medium

The present disclosure provides a training method for a tag identification network, a tag identification apparatus/method and device. The training method for a tag identification network includes: acquiring a first number of first images, each of the first images having its standard tag; adjusting parameters of the tag identification network by utilizing the first number of first images and their standard tags; selecting a second number of first images from the first number of first images as images to be converted, the second number being smaller than the first number; performing a style conversion process on each of images to be converted to generate a second image corresponding thereto, and serving a standard tag of the image to be converted as a standard tag of the second image; and adjusting the parameters of the tag identification network by utilizing the second number of the second images and their standard tags.

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Beijing, China

Guangwei Huang