CN113159015A - Seal identification method based on transfer learning - Google Patents
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Abstract
The invention discloses a seal recognition method based on transfer learning, which comprises S1 seal image collection, S2 original seal image database construction, S3 training stage and S4 recognition stage, wherein in the step S2, the preprocessing comprises an RGB color model extraction method and a binarization expansion corrosion method. The invention belongs to the technical field of seal identification methods, and particularly provides a seal identification method based on transfer learning, which can improve the accuracy of original seal image identification by performing a color model extraction method and an expansion corrosion method on a seal image with a background color, and can effectively improve the effect of deep neural network training by dividing a training set, a verification set and a test set, improve the training efficiency and increase the identification accuracy.
Description
Technical Field
The invention belongs to the technical field of seal identification methods, and particularly relates to a seal identification method based on transfer learning.
Background
With the advent of the big data age, people can more easily obtain a large amount of data. In addition, as the field of machine learning continues to develop, the problem of how to make computers have the ability to work in reverse, how to make large amounts of data work better, becomes very practical and valuable. To solve these problems, transfer learning has been proposed and increasingly receives attention.
In the identification technology of transaction bills, the identification of the seal is a relatively critical ring. For the seal authenticity identification, a pattern comparison mode is usually adopted, for example, a seal pattern folding comparison mode or an electronic pattern analysis comparison mode is adopted.
The existing seal identification method used by banks is mostly manual operation, the seal on a check is compared with an original seal image left by a client through manual angle folding, and the original comparison method has the defects of multiple artificial factors, poor accuracy, large workload and the like. In addition, the traditional folding comparison of the seal patterns and the analysis and comparison of the existing electronic patterns have error rate in a certain ratio, if the comparison requirements of the patterns are strict, the true and false seals can not pass the verification, and if the comparison requirements are loose, the true and false seals can pass the verification, so that the seal identification method is inconvenient and is not beneficial to accurately identifying the seal.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a seal recognition method based on transfer learning, which can improve the accuracy of original seal image recognition by performing a color model extraction method and an expansion corrosion method on a seal image with a background color, and can effectively improve the effect of deep neural network training by dividing a training set, a verification set and a test set, improve the training efficiency and increase the recognition accuracy.
The technical scheme adopted by the invention is as follows: the invention relates to a seal identification method based on transfer learning, which comprises the following steps:
s1 stamp image collection: collecting a seal image of a client, and scanning the seal image into a computer;
s2, constructing an original stamp image database: judging whether the stamp image in the step S1 contains a background image except the original stamp, if so, preprocessing the stamp image, then constructing the preprocessed original stamp image into an original stamp image database, and if not, directly storing the stamp image into the original stamp image database, wherein the original stamp image database contains original stamp image data;
s3 training stage: acquiring a pre-training model based on a convolutional neural network, taking an original seal image database as the input of the pre-training model, training to construct a classifier based on the convolutional neural network, and updating the classifier by using the output of the pre-training model;
s4 identification phase: and (4) carrying out seal identification by using the classifier selected in the step (S3) so as to obtain an identification result.
Further, in step S1, the stamp image is photographed or scanned by a camera or a scanner, so as to obtain stamp image data.
Further, the preprocessing in step S2 includes an RGB color model extraction method and a binarization expansion etching method, where the RGB color model extraction method is used to process a red stamp image, and the binarization expansion etching method is used to process a blue stamp image.
Further, the specific step of step S3 includes:
(1) firstly, dividing an original seal image data set into a training set, a verification set and a test set, inputting pictures of the training set into a pre-training model, obtaining a plurality of characteristic graphs after each picture passes through each convolution layer and each pooling layer in the pre-training model, and obtaining a prediction result after the characteristic graphs pass through each full-connection layer;
(2) then, evaluating the difference between the prediction result and the true value by using a cross entropy function, repeating the steps for each picture in the training set, in the process, optimizing and updating one or more full link layer parameters of the model by using a gradient descent method each time, and inputting all training picture samples into the network to update the network once;
(3) after each network updating, calculating the accuracy by using the verification set, and selecting the model with the highest accuracy to obtain a classifier;
(4) and obtaining a plurality of classifiers through multiple steps, verifying the accuracy of each classifier by using the test set, and selecting the classifier with the highest accuracy.
Further, the RGB color model extraction method includes the steps of:
(1) extracting a seal image; if Red in the pixel values of the stamp image is more than 100, and the differences between the pixel values Red and Blue and the differences between Red and Green are both more than 45, the pixel point is considered as an effective stamp color, namely Red; if the sum of the three component values is larger than 540, the pixel point is considered as a normal background color; except for the two situations, the pixel points are background interference colors;
(2) and removing noise information on the stamp image by using Hough change and median filtering to obtain the original stamp image.
Further, the binarization expansion corrosion method comprises the following steps:
(1) after the seal image is subjected to binarization processing, calculating background identification discrete points in the seal image, acquiring an image frame of the background identification discrete points, and repeatedly expanding the image frame of the background identification discrete points to obtain an image with continuous edges;
(2) performing multiple corrosion on the image subjected to repeated expansion; and removing the background of the corroded image, and marking a target of a closed edge to obtain an original seal image.
The beneficial effects obtained by adopting the scheme are as follows: according to the seal recognition method based on transfer learning, the accuracy of original seal image recognition can be improved through a color model extraction method and an expansion corrosion method for the seal image with the background color, the effect of deep neural network training can be effectively improved, the training efficiency is improved, and the recognition accuracy is improved.
Drawings
Fig. 1 is a diagram of a process flow for organizing a seal identification method based on transfer learning according to the present invention.
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to a seal identification method based on transfer learning, which comprises the following steps:
s1 stamp image collection: collecting a seal image of a client, and scanning the seal image into a computer;
s2, constructing an original stamp image database: judging whether the stamp image in the step S1 contains a background image except the original stamp, if so, preprocessing the stamp image, then constructing the preprocessed original stamp image into an original stamp image database, and if not, directly storing the stamp image into the original stamp image database, wherein the original stamp image database contains original stamp image data;
s3 training stage: acquiring a pre-training model based on a convolutional neural network, taking an original seal image database as the input of the pre-training model, training to construct a classifier based on the convolutional neural network, and updating the classifier by using the output of the pre-training model;
s4 identification phase: and (4) carrying out seal identification by using the classifier selected in the step (S3) so as to obtain an identification result.
In step S1, the stamp image is photographed or scanned by a camera or a scanner to obtain stamp image data.
The preprocessing in the step S2 includes an RGB color model extraction method for processing a red stamp image and a binarization expansion etching method for processing a blue stamp image.
The specific steps of step S3 include:
(1) firstly, dividing an original seal image data set into a training set, a verification set and a test set, inputting pictures of the training set into a pre-training model, obtaining a plurality of characteristic graphs after each picture passes through each convolution layer and each pooling layer in the pre-training model, and obtaining a prediction result after the characteristic graphs pass through each full-connection layer;
(2) then, evaluating the difference between the prediction result and the true value by using a cross entropy function, repeating the steps for each picture in the training set, in the process, optimizing and updating one or more full link layer parameters of the model by using a gradient descent method each time, and inputting all training picture samples into the network to update the network once;
(3) after each network updating, calculating the accuracy by using the verification set, and selecting the model with the highest accuracy to obtain a classifier;
(4) and obtaining a plurality of classifiers through multiple steps, verifying the accuracy of each classifier by using the test set, and selecting the classifier with the highest accuracy.
The RGB color model extraction method comprises the following steps:
(1) extracting a seal image; if Red in the pixel values of the stamp image is more than 100, and the differences between the pixel values Red and Blue and the differences between Red and Green are both more than 45, the pixel point is considered as an effective stamp color, namely Red; if the sum of the three component values is larger than 540, the pixel point is considered as a normal background color; except for the two situations, the pixel points are background interference colors;
(2) and removing noise information on the stamp image by using Hough change and median filtering to obtain the original stamp image.
The binarization expansion corrosion method comprises the following steps:
(1) after the seal image is subjected to binarization processing, calculating background identification discrete points in the seal image, acquiring an image frame of the background identification discrete points, and repeatedly expanding the image frame of the background identification discrete points to obtain an image with continuous edges;
(2) performing multiple corrosion on the image subjected to repeated expansion; and removing the background of the corroded image, and marking a target of a closed edge to obtain an original seal image.
In the training stage, the training time can be greatly shortened, corresponding training can be completed within 250-300 s, the classifier is well represented in recognition, the accuracy rate reaches more than 85%, and the accuracy of original official seal recognition is improved by respectively using a color model extraction method and an expansion corrosion method through different image characteristics represented by official seals with different colors.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (6)
1. A seal identification method based on transfer learning is characterized by comprising the following steps:
s1 stamp image collection: collecting a seal image of a client, and scanning the seal image into a computer;
s2, constructing an original stamp image database: judging whether the stamp image in the step S1 contains a background image except the original stamp, if so, preprocessing the stamp image, then constructing the preprocessed original stamp image into an original stamp image database, and if not, directly storing the stamp image into the original stamp image database, wherein the original stamp image database contains original stamp image data;
s3 training stage: acquiring a pre-training model based on a convolutional neural network, taking an original seal image database as the input of the pre-training model, training to construct a classifier based on the convolutional neural network, and updating the classifier by using the output of the pre-training model;
s4 identification phase: and (4) carrying out seal identification by using the classifier selected in the step (S3) so as to obtain an identification result.
2. The seal recognition method based on transfer learning according to claim 1, characterized in that: in the step S1, the stamp image is photographed or scanned by a camera or a scanner to obtain stamp image data.
3. The seal recognition method based on transfer learning according to claim 1, characterized in that: the preprocessing in the step S2 includes an RGB color model extraction method for processing a red stamp image and a binarization expansion etching method for processing a blue stamp image.
4. The method for identifying a seal based on transfer learning according to claim 1, wherein the specific step of step S3 includes:
(1) firstly, dividing an original seal image data set into a training set, a verification set and a test set, inputting pictures of the training set into a pre-training model, obtaining a plurality of characteristic graphs after each picture passes through each convolution layer and each pooling layer in the pre-training model, and obtaining a prediction result after the characteristic graphs pass through each full-connection layer;
(2) then, evaluating the difference between the prediction result and the true value by using a cross entropy function, repeating the steps for each picture in the training set, in the process, optimizing and updating one or more full link layer parameters of the model by using a gradient descent method each time, and inputting all training picture samples into the network to update the network once;
(3) after each network updating, calculating the accuracy by using the verification set, and selecting the model with the highest accuracy to obtain a classifier;
(4) and obtaining a plurality of classifiers through multiple steps, verifying the accuracy of each classifier by using the test set, and selecting the classifier with the highest accuracy.
5. The seal recognition method based on transfer learning of claim 3, wherein the RGB color model extraction method comprises the following steps:
(1) extracting a seal image; if Red in the pixel values of the stamp image is more than 100, and the differences between the pixel values Red and Blue and the differences between Red and Green are both more than 45, the pixel point is considered as an effective stamp color, namely Red; if the sum of the three component values is larger than 540, the pixel point is considered as a normal background color; except for the two situations, the pixel points are background interference colors;
(2) and removing noise information on the stamp image by using Hough change and median filtering to obtain the original stamp image.
6. The seal recognition method based on transfer learning according to claim 3, characterized in that: the binarization expansion corrosion method comprises the following steps:
(1) after the seal image is subjected to binarization processing, calculating background identification discrete points in the seal image, acquiring an image frame of the background identification discrete points, and repeatedly expanding the image frame of the background identification discrete points to obtain an image with continuous edges;
(2) performing multiple corrosion on the image subjected to repeated expansion; and removing the background of the corroded image, and marking a target of a closed edge to obtain an original seal image.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113887444A (en) * | 2021-10-08 | 2022-01-04 | 财悠悠科技(深圳)有限公司 | Method for advanced treatment of positive and negative convolution neural network of image-text impression |
| CN113954360A (en) * | 2021-10-25 | 2022-01-21 | 华南理工大学 | 3D printing product anti-counterfeiting method based on embedded identification code multi-process application |
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- 2021-05-07 CN CN202110494953.8A patent/CN113159015A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113887444A (en) * | 2021-10-08 | 2022-01-04 | 财悠悠科技(深圳)有限公司 | Method for advanced treatment of positive and negative convolution neural network of image-text impression |
| CN113887444B (en) * | 2021-10-08 | 2024-07-26 | 财悠悠科技(深圳)有限公司 | Advanced treatment method for image-text impression forward and backward convolution neural network |
| CN113954360A (en) * | 2021-10-25 | 2022-01-21 | 华南理工大学 | 3D printing product anti-counterfeiting method based on embedded identification code multi-process application |
| WO2023071159A1 (en) * | 2021-10-25 | 2023-05-04 | 华南理工大学 | 3d printed product counterfeiting prevention method based on embedded identification code and applicable to multiple processes |
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