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Sample labeling method and computer storage medium

A sample labeling and sample image technology, applied in the computer field, can solve problems such as low labeling accuracy and achieve the effect of improving accuracy

Active Publication Date: 2019-08-16
BEIJING CENTURY TAL EDUCATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, an embodiment of the present invention provides a sample labeling method and a computer storage medium to solve the problem of low labeling accuracy of real samples in the prior art

Method used

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  • Sample labeling method and computer storage medium
  • Sample labeling method and computer storage medium

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Experimental program
Comparison scheme
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Embodiment 1

[0013] figure 1 It is a schematic flowchart of a sample labeling method provided in Embodiment 1 of the present invention. Such as figure 1 As shown, the sample labeling methods include:

[0014] S101: Obtain a sample image to be labeled.

[0015] The sample images to be labeled can be used for subsequent training of the machine learning model and used as training sample images. In the embodiment of the present invention, the training sample image is an image including characters, where the characters include but not limited to: characters, letters, numbers, and symbols.

[0016] S102: Perform character detection on the sample image to be labeled by using the character detection model, and obtain a character frame for indicating the character position of each character in the sample image to be labeled.

[0017] The character detection model is used to detect characters in the sample images to be annotated. The character detection model can be used by those skilled in the...

Embodiment 2

[0036] Such as figure 2 As shown, it shows a schematic flowchart of the sample labeling method according to Embodiment 2 of the present invention. The sample labeling methods include:

[0037] S201: Obtain a sample image to be labeled.

[0038] The sample images to be labeled can be used for subsequent training of the machine learning model and used as training sample images. In the embodiment of the present invention, the training sample image is an image including characters, where the characters include but not limited to: characters, letters, numbers, and symbols.

[0039] S202: Perform character detection on the sample image to be labeled by using the character detection model, and obtain a character frame for indicating the character position of each character in the sample image to be labeled.

[0040] The character detection model is used to detect characters in the sample images to be annotated. The character detection model can be used by those skilled in the ar...

Embodiment 3

[0080] According to an embodiment of the present invention, a computer storage medium is provided, and the computer storage medium stores: an instruction for acquiring a sample image to be labeled; and is used for performing character detection on the sample image to be labeled through a character detection model , and obtain an instruction for character frames indicating the character positions of each character in the sample image to be labeled; an instruction for determining the average width of a character frame according to the number of character frames and the width of each character frame; The average width of the character frame is an instruction to filter out at least one first candidate character frame from all character frames; for each first candidate character frame, in the horizontal direction, determine the minimum distance from the current first candidate character frame character frame as the current second candidate character frame, in the vertical direction,...

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Abstract

The invention provides a sample labeling method and a computer storage medium. The sample labeling method comprises the steps: obtaining a sample image; performing character detection on the sample image through a character detection model to obtain a character frame of the character position of each character in the sample image; determining the average width of the character frames according tothe number of the character frames and the width of each character frame; screening out at least one first candidate character box according to the average width of the character boxes; determining acharacter box with the minimum distance from the current first candidate character box as a current second candidate character box in the horizontal direction, and determining a character box with theminimum distance from the current first candidate character box and the width greater than a first reference value as a reference character box in the vertical direction; if the vertical center lineof the current second candidate character box in the vertical direction passes through the reference character box, determining the current first candidate character box and the current second candidate character box as candidate character boxes to be combined; and generating labeling information of the sample image according to the processing of the candidate character boxes to be merged.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a sample labeling method and a computer storage medium. Background technique [0002] With the development of artificial intelligence and machine learning technology, more and more fields have begun to use machine learning methods to train equipment to have a certain degree of intelligence. With this comes a growing demand for training samples. For example, when training optical character detection models and recognition models, a large number of labeled samples are required. Labeled samples refer to manually labeling character boxes and character categories used to indicate character positions on real samples. The existing method of using purely manual labeling when obtaining real samples has the problem of low efficiency, and because of manual labeling, there will be a certain loss of accuracy, such as manual errors that cause inaccurate character position labeling and chara...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/34
CPCG06V30/158G06V30/153
Inventor 兴百桥
Owner BEIJING CENTURY TAL EDUCATION TECH CO LTD
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