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Text real-time positioning recognition method based on deep learning attention mechanism

A real-time positioning and deep learning technology, applied in the field of text recognition, can solve the problems of low recognition accuracy, difficulty in locating text regions, and poor robustness of character segmentation, so as to speed up training, avoid inaccurate detection, and improve recognition. The effect of precision

Active Publication Date: 2019-12-13
安徽省科亿信息科技有限公司
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AI Technical Summary

Problems solved by technology

[0009] Therefore, OCR recognition in the prior art has the problems of difficulty in locating text regions in complex backgrounds, poor character segmentation robustness, and low recognition accuracy. Therefore, it is urgent to provide a new text recognition method to solve the above problems

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  • Text real-time positioning recognition method based on deep learning attention mechanism
  • Text real-time positioning recognition method based on deep learning attention mechanism
  • Text real-time positioning recognition method based on deep learning attention mechanism

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Embodiment Construction

[0042] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, so as to define the protection scope of the present invention more clearly.

[0043] see figure 1 , the embodiment of the present invention includes:

[0044] A text real-time location recognition method based on deep learning attention mechanism, comprising the following steps:

[0045] S1: Build a text image acquisition system, collect training samples and manually mark them, and establish an OCR data set;

[0046]For the collected training samples, delete invalid images and manually label them. Randomly select 80,000 images as the test set and about 20,000 remaining images as the training set. Use text files to store the labeling information of each picture, and use endpoints Named in the form of coordinates plus text...

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Abstract

The invention discloses a text real-time positioning recognition method based on a deep learning attention mechanism. The method comprises the following steps: establishing a text image acquisition system, collecting a training sample, performing manual annotation, establishing an OCR data set, preprocessing an image according to the characteristics of the OCR data set, then proposing a deep significance attention network to locate a text area, and distinguishing a text from a complex background; finally, identifying the text by utilizing deep convolution circulation attention, thereby realizing real-time detection and identification of the text image. Characters do not need to be segmented, the detection and recognition precision of the OCR system can be effectively improved, good real-time performance and mobility are achieved, and wide application prospects are achieved.

Description

technical field [0001] The invention relates to the technical field of text recognition, in particular to a real-time text positioning and recognition method based on a deep learning attention mechanism. Background technique [0002] OCR technology is the abbreviation of Optical Character Recognition (Optical Character Recognition). It converts the text of various bills, newspapers, books, manuscripts and other printed materials into image information through optical input methods such as scanning, and then uses text recognition technology to convert the image information. Enter technology for computers that can be used. It can be applied to the input and processing fields of bank bills, a large amount of text materials, archives, and copywriting. It is suitable for automatic scanning identification and long-term storage of a large number of bill forms in banking, taxation and other industries. [0003] The prior art generally includes four steps: image preprocessing, text...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/20G06K9/62
CPCG06V10/22G06F18/241
Inventor 汪从玲
Owner 安徽省科亿信息科技有限公司
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