Certificate information detection and extraction method

An extraction method and information detection technology, which is applied in the fields of image processing and text recognition, can solve problems such as lack of generalization performance, high image quality requirements, complex rules, etc., and achieve strong adaptability, excellent precision, and easy recognition and matching.

Pending Publication Date: 2020-07-28
NANJING UNIV
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AI Technical Summary

Problems solved by technology

Relying on rules for text classification has two disadvantages: (1) it does not have generalization performance, and a set of rules

Method used

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  • Certificate information detection and extraction method
  • Certificate information detection and extraction method
  • Certificate information detection and extraction method

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Embodiment Construction

[0027] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0028] Such as figure 1 As shown, the document information detection and extraction method is used to identify the driver's license, input the driver's license picture to the Faster-RCNN model, obtain the cut text area picture, and know the text area picture category, and then use the text area picture as the CRNN model input to get specific structured text recognition results.

[0029] In this embodiment, the detection and recognition of the driver's license name, gender, license number, driver'...

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Abstract

The invention discloses a certificate information detection and extraction method. A synthesized certificate data set is used to replace a real certificate data set for the training of a Faster-RCNN detection model; certificate information is detected and classified by using a method based on a deep neural network, and special preprocessing is not needed; and an end-to-end character recognition method is used for recognizing the certificate character information with the uncertain length, and segmentation is not needed. The character length does not need to be set, a single Chinese character does not need to be recognized, the influence on the recognition rate due to segmentation errors is avoided, and the text information can be obtained only by inputting a picture into the network model.Compared with a traditional method based on character template matching, the method has strong adaptability when facing the problems of low brightness, low contrast, uneven illumination, deformation,incompleteness, shielding and the like, and the precision of the method is far superior to that of the traditional method.

Description

technical field [0001] The invention relates to a certificate information detection and extraction method, which belongs to the technical fields of image processing and character recognition. Background technique [0002] Deep neural network is a complex mathematical model. It is a mode of deep learning. It consists of layers of network layers. The input data gets output data after passing through all network layers. According to the difference between the output data and the labeled data, it can be Constructing a loss function, and then backpropagating the gradient of the loss function, the weights of the network layer can be updated, thereby further reducing the difference between the output data and the labeled data. Among them, the data set used for input and the corresponding labeled data constitute the training data set of the deep neural network, and the function and performance of the deep neural network are related to the network structure and the training data set....

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/62G06V30/153G06V30/287G06V30/10G06N3/045G06F18/241G06F18/214
Inventor 俞扬詹德川周志华韦天健
Owner NANJING UNIV
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