Identity card number detection method and device, readable storage medium and terminal

Abstract

The invention provides an identity card number detection method and device, a readable storage medium and a terminal. The method comprises steps of firstly employing a target detection algorithm to locate the position of an identity card, extracting an image in a detection frame, carrying out the size normalization of the image, selecting a mask with a proper size to carry out the morphological operation of the image, and enabling an identity card number region to be communicated; adaptively selecting a threshold value to realize binaryzation, acquiring a straight line by using hough, selecting a main direction of an image and rotating, and extracting a connected region with a length-width ratio meeting requirements and an identity card number region. The method is advantaged in that inclination correction of the identity card is not needed, identity card information extraction in a complex shooting scene can be improved, the recognition rate is greatly improved, and the method can be widely applied to the fields of security, finance and the like.

Description

technical field [0001] The invention relates to the technical field of information detection or intelligent vision, in particular to an ID card number detection method, device, readable storage medium and terminal. Background technique [0002] With the rise of artificial intelligence, image recognition technology is gradually applied to security, military, medical, intelligent transportation and other fields, and technologies such as face recognition and fingerprint recognition are increasingly used in public security, finance, aerospace and other security fields. In the military field, image recognition is mainly used in the detection and identification of targets, through automatic image recognition technology to identify and strike enemy targets; in the medical field, image recognition technology can be used for various medical image analysis and diagnosis, On the one hand, it can greatly reduce the cost of medical care, and on the other hand, it can also help to improve...

AI Technical Summary

Problems solved by technology

This method is currently a popular character detection method, but for the document number detection task, this method has the following defects: (1) Non-document area images also participate in the network reasoning process, which wastes computing resources on the one hand, and on the other hand, for non-document Misdetection of character presence in the region requires additional processing logic to eliminate; (2) The calculation resource consumption of this scheme is relatively large, and the training and reasoning time is longer than this proposal; (3) Due to the uninterpretable behavior of the neural network, this method The frame of the positioned character area cannot accurately locate the smallest circumscribed rectangular frame of the character, and even part of the character area will be cut off. That is, the traditional document image optical recognition (OCR) technology is mainly for high-definition scanned images. This method requires the recognized image to have a clean background, use standard typography and have high resolution

However, in natural scenes, there are problems such as large text background noise, irregular text distribution, and the influence of natural light sources. The detection rate of OCR technology in actual natural scenes is not ideal. The identification of ID cards and other documents will bring pressure to the character recognition in the subsequent steps.

[0006] To sum up, in the existing ID card recognition technology, the quality inspection video has extremely poor clarity, the environment is extremely complex, and the quality inspection requirements are diverse. The current algorithms all solve a single problem, such as recognizing faces and text information from high-quality images , it is difficult to directly translate into complex business judgments. Therefore, a detection technology that can efficiently identify documents in complex environments with complex backgrounds in non-document areas or small gradient changes in document foreground areas is needed.

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