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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 the problems of low efficiency of manual labeling and poor labeling effect, and achieve the effect of high character detection accuracy, avoiding heavy workload and improving recognition 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 problems of low efficiency and poor labeling effect 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 This is a schematic flowchart of a sample labeling method provided in Embodiment 1 of the present invention. Such as figure 1 As shown, according to the embodiment of the present invention, the sample labeling method includes:

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

[0015] Among them, the sample image to be labeled is used for subsequent training of the machine learning model as a training sample image. In the embodiment of the present invention, the training sample image is an image including character information, where the characters include, but are not limited to: text, letters, numbers, and symbols.

[0016] S102: Perform connected domain analysis and character category recognition on the sample image to be labeled, and generate a first detection and recognition result, where the first detection and recognition result includes a first character position indicating the position of each character in the sample image to be labeled Information and in...

Embodiment 2

[0037] figure 2 This is a schematic flowchart of a sample labeling method provided in Embodiment 2 of the present invention. Such as figure 2 As shown, according to the embodiment of the present invention, the sample labeling method includes:

[0038] S201: Obtain a sample image to be labeled.

[0039] Among them, the sample image to be labeled is used for subsequent training of the machine learning model as a training sample image. In the embodiment of the present invention, the training sample image is an image including character information, where the characters include, but are not limited to: text, letters, numbers, and symbols.

[0040] S202: Determine whether there is a labeled data file corresponding to the sample image to be labeled.

[0041] The first thing to note is that this step is optional.

[0042] The marked data file includes information of marked character positions of the sample image to be marked and information of marked character types. The information of ...

Embodiment 3

[0074] According to an embodiment of the present invention, a computer storage medium is provided, the computer storage medium stores: an instruction for obtaining a sample image to be labeled; for performing connected domain analysis and character classification on the sample image to be labeled Recognize and generate a first detection and recognition result, where the first detection and recognition result includes instructions for indicating the first character position of each character in the sample image to be labeled and the first character category information ; Instructions for determining whether there is a first neural network model for detecting the position of characters in the image and a second neural network model for recognizing characters in the image; if it exists, through the first neural network The model and the second neural network model perform character detection and recognition on the sample image to be labeled, and generate a second detection and reco...

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PUM

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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 connected domain analysis and character class identification on the sample image to generate a first detection identification result which comprises information for indicating a first character position of each character and informationof a first character class; determining whether a first neural network model and a second neural network model exist; if so, performing character detection and recognition on the sample image throughthe first neural network model and the second neural network model, and generating a second detection and recognition result which comprises information of a second character position of each character and information of a second character category; comparing the first character position with the second character position, comparing the first character category with the second character category,and determining a character position labeling result and a character category labeling result according to a comparison result; and generating labeling information of the sample image according to the character position labeling result and the character category labeling result.

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 embed machine learning methods into equipment to make them have a certain degree of intelligence. This is followed by an increase in demand for training samples for machine learning training. For example, when training the optical character detection model and the recognition model, a large number of labeled samples are required. The labeled sample refers to marking the character frame and character category used to indicate the position of the character on the real sample. [0003] In the prior art, a purely manual labeling method is used when obtaining real samples. This method relies on manual labor, so the labeling efficiency is low; moreover, because manual labeling will have a certain accurac...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32
CPCG06V30/40G06V20/62G06V30/10
Inventor 兴百桥
Owner BEIJING CENTURY TAL EDUCATION TECH CO LTD