Banknote management method and system thereof

Abstract

The present invention provides a banknote management method, which includes collecting, identifying and processing banknote features by using a banknote information processing device to obtain banknote feature information; combining the banknote feature information, business information, and information of the banknote information processing device together transmitted to the main control server; the main control server processes the received information and classifies the banknotes. The invention also provides a corresponding banknote management system. The above method of the present invention can improve the robustness of recognition while ensuring the operation speed, and ensure the accuracy and practicability in practical applications.

Description

technical field [0001] The invention belongs to the field of finance, and in particular relates to a banknote management system and a method thereof. Background technique [0002] With the continuous improvement of the application level of financial information, the currency anti-counterfeiting, business process management and financial security of the banking system are gradually becoming intelligent. Banknote distribution management, damaged currency management and cash storage management are of great significance. [0003] Banknote management is mainly for the comprehensive processing of banknote information, business information and other information. The serial numbers in banknote information play an increasingly important role in banknote management. By associating the serial number information with business information, you can Greatly facilitate banknote tracking and inquiry. This makes the collection and identification of serial numbers and other information in ba...

AI Technical Summary

Problems solved by technology

In the DSP identification method, it is often limited to the network transmission efficiency and the influence on the position and orientation of the banknote in the DSP identification, and the identification efficiency and the robustness of the identification algorithm are relatively poor. For example, in the patent application number CN201510702688.2 In the application, the edge is fitted through the gray threshold and direction search, and then the edge line is screened through the threshold to obtain the slope of the area. Combined with neural network training to identify the face, it is identified through progressive scanning and subsequent neural network. Serial number

[0005] Another example is in a prior art, such as the paper "Research and Implementation of RMB Sorting Method Based on Image Analysis", which has adopted the method of convolutional neural network to identify the serial number. However, in the above-mentioned scheme, only the most Simple binarization divides the characters, which cannot effectively tighten the characters, and this will directly affect the amount of data that needs to be processed in the follow-up, and directly affect the practical value of the algorithm; The size processing does not effectively tighten the preprocessed and segmented images and effectively normalize the image data, and this simple size processing will bring heavy data processing to the subsequent neural network recognition, which is extremely difficult. The subsequent identification efficiency is greatly reduced; and, the above-mentioned technical solutions also do not deal well with the impact of banknote incompleteness on banknote identification and image processing

Although the above-mentioned technical solution can theoretically achieve a certain recognition accuracy, due to its low efficiency of calculation and recognition, it cannot be well transformed into a commercial practical method, and cannot meet the speed requirements in real banknote recognition.

[0006] It can be seen that the existing technology has the following problems: the orientation of banknotes and the effective positioning of characters cannot be efficiently solved, the range of characters after recognition is relatively large, and it is easy to cause wrong division of characters, and the amount of data required for later image processing and recognition is large , which reduces the recognition efficiency; it cannot adapt well to the rapid tilt change of the banknote image caused by banknotes, and cannot correct and identify the tilt of the banknote in time; the robustness of the identification of damaged banknotes is low, and no corresponding banknote damage is provided. Identify processing method

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