A natural scene text detection method based on an attention mechanism convolutional neural network

A convolutional neural network and natural scene technology, applied in the field of natural scene text detection, can solve the problems of low text recall rate and low recognition accuracy rate, and achieve the effect of solving slow running speed and improving detection accuracy

Active Publication Date: 2019-01-08
FUZHOU UNIV
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Problems solved by technology

[0015] In view of this, the purpose of the present invention is to propose a natural scene text detection method based on attention mechanism convolutional neural network to solve the problems of low text recall rate and

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  • A natural scene text detection method based on an attention mechanism convolutional neural network
  • A natural scene text detection method based on an attention mechanism convolutional neural network
  • A natural scene text detection method based on an attention mechanism convolutional neural network

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Embodiment Construction

[0064] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0065] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0066] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combina...

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Abstract

The invention relates to a natural scene text detection method based on an attention mechanism convolutional neural network. Firstly, image data with text under natural scene is labeled and divided into training set and test set. Then the text image is processed as training data by data enhancement method. Based on the attention mechanism module and Inception network, a feature extraction networkis constructed, and the features of different fonts are learned by multi-scale feature fusion. The network is pre-trained using course learning strategies. The network is trained again by using the text image data of the natural scene. The fused features are adopted to regress to get the coordinates of the text in the image, and the text detection results are obtained. Finally, the validity of thetrained neural network is verified in the test set and other open data sets. The method can solve the problems of low character recall rate and low recognition accuracy under complex natural environment in the current technology, and has advantages in running speed.

Description

technical field [0001] The invention relates to the fields of pattern recognition and computer vision, in particular to a natural scene text detection method based on an attention mechanism convolutional neural network. Background technique [0002] As one of the main carriers of information transmission in people's life, text plays an extremely important role in real life. Automatic text detection provides a method for computers to obtain text information by using pictures and videos, making it possible to quickly and automatically process text information in massive natural scenes. [0003] Difficulties in automatic text detection in natural scenes include: [0004] (1) The characters in the text are of different sizes. [0005] (2) There are various fonts in the text. [0006] (3) Texts in natural scenes have complex image backgrounds. [0007] (4) Observing the text from different perspectives. [0008] (5) Diversity of illuminance. [0009] Existing research has p...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46G06N3/04
CPCG06V10/40G06V30/10G06N3/045G06F18/214G06F18/253
Inventor 柯逍罗洁
Owner FUZHOU UNIV
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