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Text detection method, device, electronic device and computer storage medium

A text detection and text technology, applied in the computer field, can solve problems such as inaccurate detection results, achieve the effects of saving computing resources, reducing the amount of computing, and improving accuracy

Active Publication Date: 2021-02-23
BEIJING YIZHEN XUESI EDUCATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the embodiment of the present application provides a text detection method, device, electronic equipment and computer storage medium to overcome the defect of inaccurate detection results when detecting text in the prior art

Method used

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  • Text detection method, device, electronic device and computer storage medium
  • Text detection method, device, electronic device and computer storage medium
  • Text detection method, device, electronic device and computer storage medium

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Experimental program
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Embodiment 1

[0028] Embodiment 1 of the present application provides a text detection method, such as figure 1 as shown, figure 1 It is a flow chart of a text detection method provided in the embodiment of the present application, and the text detection method includes the following steps:

[0029] Step S101 , perform feature extraction on the text image to be detected, and obtain a horizontal area probability map and a vertical area probability map corresponding to at least one text area in the text image to be detected.

[0030] It should be noted that the text detection method in the embodiment of the present application is applicable to text detection with various text densities, including but not limited to regular density text, dense density text, sparse density text, especially dense density text. Among them, specific indicators for determining whether a certain text is a dense text can be appropriately set by those skilled in the art according to the actual situation, including bu...

Embodiment 2

[0041] Embodiment 2 of the present application is based on the solution of Embodiment 1. Optionally, in an embodiment of the present application, step S103 may be implemented as the following step S103a and step S103b.

[0042] Exemplarily, in step S103a, the connected domains are respectively calculated for the horizontal region binary graph and the vertical region binary graph, and corresponding at least one horizontal connected domain and at least one vertical connected domain are obtained; step S103b, according to at least one horizontal connected domain and at least A vertically connected domain to obtain the text detection results of the text image to be detected.

[0043] A text region corresponds to a horizontal connected domain and a vertical connected domain. For example, if a text image to be detected includes 100 text regions, after calculating the connected domains respectively for the horizontal region binary map and the vertical region binary map, 100 horizontal...

Embodiment 3

[0057] Embodiment 3 of the present application is based on the solutions of Embodiment 1 and Embodiment 2, wherein step S101 can also be implemented as the following steps S101a-step S101d.

[0058] Step S101a, performing first text feature extraction on the text image to be detected.

[0059] In the embodiment of the present application, when performing feature extraction on the text image to be detected, the text image to be detected can be input into the residual network part (such as the Resnet network) to extract the first text feature, such as extracting texture, edge, and corner points from the input image and semantic information, which are represented by 4 sets of feature maps of different sizes. Take the text image to be detected as the original image, and the Resnet network extracts the features of the original image as an example. The Resnet18 network is constructed by connecting four blocks in series. Each block includes several layers of convolution operations. T...

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Abstract

Embodiments of the present application provide a text detection method, device, electronic equipment, and computer storage medium. The text detection method includes: performing feature extraction on a text image to be detected to obtain a horizontal area corresponding to at least one text area in the text image to be detected Probability map and vertical area probability map; binarize the horizontal area probability map and vertical area probability map to obtain the corresponding horizontal area binary map and vertical area binary map; horizontal area binary map and vertical area binary map Calculate the connected domain, and obtain the text detection result of the text image to be detected according to the connected domain. In this application, the connected domain is calculated from the horizontal area binary image and the vertical area binary image corresponding to the text area, and then the text detection result is obtained according to the connected area. For two cohesive text areas, they can be divided according to their respective connected domains. The glued areas are divided into respective text areas, which improves the accuracy of text detection.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technologies, and in particular to a text detection method, device, electronic equipment, and computer storage medium. Background technique [0002] Text detection is a technology that detects text regions in images and marks their bounding boxes. Text detection has a wide range of applications and is a pre-step for many computer vision tasks, such as image search, text recognition, identity authentication, and visual navigation. [0003] The main purpose of text detection is to locate the position of text lines or characters in the image. At present, a popular text detection method is based on the method of computing connected domains, also known as the method based on segmentation ideas. This method is based on the fully convolutional neural network. The network model extracts image features, then binarizes the feature map and calculates its connected domain, and then determin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/20G06K9/34G06T7/187
CPCG06T7/187G06T2207/10004G06V10/145G06V10/267
Inventor 李盼盼秦勇
Owner BEIJING YIZHEN XUESI EDUCATION TECH CO LTD
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