Text recognition model training method and device, text recognition method and device and equipment

A text recognition and training method technology, applied in the field of text recognition, can solve problems such as the inability to meet the needs of the rapid development of computer vision tasks, the difficulty in achieving both recognition speed and recognition accuracy, and the inability of algorithms to be directly transferred to text detection.

Active Publication Date: 2021-04-09
BEIJING YIZHEN XUESI EDUCATION TECH CO LTD
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Text detection and recognition have a wide range of applications, and are the pre-steps of many computer vision tasks, such as image search, identity authentication, and visual navigation. The main purpose of text detection is to locate the position of text lines or characters in the image, while text recognition is Transcribing an image with text lines into a string (recognizing its content), precise positioning and accurate recognition of text is both very important and challenging, because compared to general object detection and

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  • Text recognition model training method and device, text recognition method and device and equipment
  • Text recognition model training method and device, text recognition method and device and equipment
  • Text recognition model training method and device, text recognition method and device and equipment

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Embodiment 1

[0089] refer to figure 1 , the embodiment of the present invention provides a kind of training method of text recognition model, it is characterized in that, comprises:

[0090] Step S1: Construct the initial model, which consists of basic parts such as convolutional neural network (Convolutional Neural Networks, CNN), recurrent neural network (Recurrent Neural Network, RNN) and word embedding module. The convolutional neural network includes the first convolutional neural network Network and the second convolutional neural network, the initial model includes the first part and the second part, the first part and the second part are composed of a plurality of basic parts, wherein:

[0091] The first part is used to recognize the text content in the image, the first part has a first convolutional neural network and a recurrent neural network;

[0092] The second part is used to judge whether a given text is in a given image, and the second part has a word embedding module and ...

Embodiment approach

[0113] As an implementation manner, the second convolutional neural network includes four Block blocks arranged in sequence;

[0114] In this embodiment, by inputting the third text image into the second convolutional neural network, feature extraction is performed through each Block block in the second convolutional neural network, and after the feature map is obtained, the feature maps of the four Block blocks are respectively passed Downsampling or upsampling processing, respectively obtain the processed feature map, and the processed feature map of the four Block blocks has the same second size, in order to make the output of the second convolutional neural network meet the input of the word embedding module The same length, in this embodiment, the feature maps of the first two Block blocks in the four Block blocks are respectively down-sampled, and the feature map of the last Block block is up-sampled to obtain the processed feature map, so that The second size is 1 / 16 of...

Embodiment 2

[0125] refer to Figure 5 , the embodiment of the present invention provides a training device for a text recognition model, including:

[0126] an initial model building block for constructing an initial model;

[0127] The initial model training module is used to use the output of the first text image data through the cyclic neural network as the string input of the word embedding module, and based on the first text image data through the second convolutional neural network of the second part The first feature map obtained after is another input of the word embedding module, and the initial model is trained to obtain a convergent initial model;

[0128] A text recognition model acquisition module, configured to acquire a text recognition model based on the converged initial model;

[0129] Wherein, the initial model includes:

[0130] The first part is used to identify the text content in the image, the first part has a first convolutional neural network and a recurrent n...

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Abstract

The invention provides a text recognition model training method and device, a text recognition method and device, and equipment. The training method comprises the following steps: constructing an initial model; taking the output of the first text image data through the recurrent neural network as the character string input of the word embedding module, taking a first feature map obtained after the first text image data passes through a second convolutional neural network of the second part as the other input of the word embedding module, and training the initial model to obtain a convergent initial model; and obtaining a text recognition model based on the converged initial model; the initial model comprises a first part used for recognizing text content of the image, and provided with a first convolutional neural network and a recurrent neural network and a second part used for judging whether the given text is in the given image or not, and provided with a second convolutional neural network and a word embedding module; the device is used for executing the method. By means of the training method, the text recognition model which is high in recognition speed and higher in recognition precision can be obtained.

Description

technical field [0001] The present invention relates to text recognition technology, in particular to a text recognition model training method, text recognition method, device and equipment. Background technique [0002] Text detection and recognition have a wide range of applications and are the pre-steps of many computer vision tasks, such as image search, identity authentication, and visual navigation. The main purpose of text detection is to locate text lines or characters in the image, while text recognition is Transcribing an image with text lines into a string (recognizing its content), precise positioning and accurate recognition of text is both very important and challenging, because compared to general object detection and recognition, text has multiple directions, irregular shapes, Extreme aspect ratios, fonts, colors, backgrounds and other characteristics, therefore, algorithms that are often more successful in general object detection and recognition cannot be d...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V30/40G06N3/044G06N3/045G06F18/24G06F18/214
Inventor 李自荐秦勇
Owner BEIJING YIZHEN XUESI EDUCATION TECH CO LTD
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