Bank bill identification method and system

Abstract

The invention discloses a bank bill identification method and system, and the method comprises the steps: collecting an image, and carrying out the preprocessing of the image, and obtaining a preprocessed image; performing feature extraction and recognition on the preprocessed image to obtain a recognition result; performing intelligent verification and correction on the identification result to obtain correction data; and performing data output and management on the corrected data. An intelligent verification function is integrated, the consistency of bill information can be automatically compared, and data input errors are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the field of bank bill processing, and in particular to a bank bill identification method and system. Background Art

[0002] Currently, banks rely primarily on manual processing or traditional optical character recognition (OCR) systems to process large volumes of bills. Manual processing is inefficient and prone to errors, while traditional OCR systems have low recognition rates for complex or ambiguous bill information, especially handwritten bills.

[0003] Currently, banks rely primarily on manual processing or traditional optical character recognition (OCR) systems to process large volumes of bills. Manual processing is inefficient and prone to errors, while traditional OCR systems have low recognition rates for complex or ambiguous bill information, especially handwritten bills. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a bank bill identification method and system that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a bank note recognition method is provided, the recognition method comprising:

[0006] Acquiring an image and preprocessing the image to obtain a preprocessed image;

[0007] Extracting and identifying features from the preprocessed image to obtain a recognition result;

[0008] Intelligently verifying and correcting the recognition result to obtain corrected data;

[0009] The correction data is output and managed.

[0010] Optionally, the collecting images and preprocessing the images specifically include:

[0011] Utilize a multi-angle camera system to capture receipts from different perspectives, combined with high-resolution scanning to ensure image clarity;

[0012] The preprocessing stage includes image enhancement, denoising, and edge detection.

[0013] Optionally, extracting and identifying features from the preprocessed image to obtain a recognition result specifically includes:

[0014] A deep convolutional neural network (CNN) is used to extract features from preprocessed images and combined with a specific recognition model to identify key information of the bill.

[0015] Optionally, the key information for identifying the bill specifically includes:

[0016] Use the OCR engine to analyze the image and recognize the text, numbers, barcodes or QR codes on the bill.

[0017] Optionally, the intelligent verification and correction of the recognition result to obtain the correction data specifically includes:

[0018] A rule-based verification algorithm is introduced to check the logic and consistency of the recognition results, and errors are corrected through human-computer interaction.

[0019] Optionally, the outputting and managing the corrected data specifically includes:

[0020] The identification results are automatically input into the bank system, and a detailed identification report is generated to facilitate subsequent data management and auditing.

[0021] Optionally, the generating of a detailed recognition report specifically includes: original image, recognition data and operation log.

[0022] The present invention also provides a bank bill recognition system, which applies the above-mentioned bank bill recognition method, and the recognition system includes:

[0023] An image preprocessing module is used to collect images and preprocess the images to obtain preprocessed images;

[0024] A feature extraction and recognition module is used to extract and recognize features from the pre-processed image to obtain a recognition result;

[0025] A verification and correction module, used to perform intelligent verification and correction on the recognition result to obtain correction data;

[0026] The data output module is used to output and manage the correction data.

[0027] The present invention provides a bank bill recognition method and system. The recognition method comprises: capturing an image and preprocessing the image to obtain a preprocessed image; extracting and recognizing features from the preprocessed image to obtain a recognition result; performing intelligent verification and correction on the recognition result to obtain corrected data; and outputting and managing the corrected data. The system integrates an intelligent verification function that automatically compares bill information for consistency, effectively preventing data input errors.

[0028] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 A flow chart of a bank note recognition method provided by an embodiment of the present invention;

[0031] Figure 2 A timing diagram of a bank note recognition system provided by an embodiment of the present invention;

[0032] Figure 3 A block diagram of a bank note recognition system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0034] The terms "comprises" and "comprising" and any variations thereof in the description, embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0035] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0036] like Figure 1-Figure 3 As shown, a bank note recognition method includes:

[0037] 1. Image acquisition and preprocessing: A multi-angle camera system is used to capture receipts from different perspectives, combined with high-resolution scanning to ensure image clarity. The preprocessing stage includes image enhancement, denoising, edge detection, and other techniques, laying the foundation for subsequent recognition.

[0038] 2. Feature extraction and recognition: Apply deep convolutional neural networks (CNNs) to extract features from preprocessed images and combine them with specific recognition models to identify key information on bills.

[0039] 3. Intelligent verification and correction: Introduce a rule-based verification algorithm to check the logic and consistency of the recognition results, and correct errors through human-computer interaction when necessary.

[0040] 4. Data output and management: The recognition results are automatically input into the banking system, and a detailed recognition report is generated to facilitate subsequent data management and auditing.

[0041] Device assembly

[0042] Mounting brackets and fixings:

[0043] Use appropriate brackets and mounting hardware to mount the scanner or camera, ensuring they are stable and at the correct angle to capture the best image of the receipt.

[0044] Set database connection parameters, such as database type, server address, user name, and password.

[0045] Recognition software configuration: Configure the parameters of the OCR engine, such as recognition language, character set, recognition mode, etc.

[0046] Set the parameters of the AI ​​model, including model path, input size, preprocessing steps, etc.

[0047] Adjust performance-related settings such as multi-threading, GPU acceleration options, etc.

[0048] User interface configuration: Configure the user interface to make it easy to use, including functions such as uploading receipts, viewing recognition results, and manual correction.

[0049] Set permissions and access controls to ensure only authorized users can access sensitive data and functionality.

[0050] Functional testing: Use test tickets to perform functional testing to ensure that all hardware and software components work together and are correctly identified.

[0051] Check the exception handling mechanism, such as how it behaves in cases of low-quality images, folded or damaged bills, etc.

[0052] Security and backup settings: Configure firewall and security policies to protect the system from unauthorized access.

[0053] Set up a regular backup schedule to prevent data loss.

[0054] Documentation: Record all hardware and software configuration details, including version numbers, serial numbers, and installation dates.

[0055] Create operating manuals and troubleshooting guides to help operators and maintenance personnel resolve issues.

[0056] Set OCR recognition parameters, such as recognition language, character set, and recognition mode (such as handwritten or printed text).

[0057] Configure the parameters of the machine learning model, if any, including the model path and training data location.

[0058] Adjust performance-related parameters, such as the number of threads and memory allocation, to optimize recognition speed and accuracy.

[0059] Functional testing: Testing is performed using a set of known bill samples to check the accuracy and completeness of recognition.

[0060] Test for anomalies such as low-quality images, tilted or distorted documents, and blurred printing or writing.

[0061] Confirm whether the data output is correct, including format, encoding, and integrity.

[0062] User interface configuration: Set the layout and functionality of the user interface to ensure that operators can easily upload tickets, view recognition results, and make manual corrections.

[0063] Configure permissions and roles to ensure that users at different levels can only access the functions they are authorized to.

[0064] Documentation and Training: Write detailed user manuals and maintenance guides.

[0065] Train operators to ensure they understand how to use the system and handle common problems.

[0066] Deployment and Monitoring: Deploying software in a production environment may require coordination with the IT team to ensure stable system operation.

[0067] Implement monitoring strategies, regularly check system performance and recognition accuracy, and handle faults in a timely manner.

[0068] Continuous improvement: Based on usage feedback and performance indicators, the software is regularly updated to optimize recognition algorithms and user experience.

[0069] System startup and login: Start the bank bill recognition device and ensure that all hardware devices (such as scanners, cameras, computers) are in normal working condition.

[0070] Log in to the system, enter the operator's user name and password, and enter the operation interface.

[0071] Scanning and Image Capture

[0072] Place the bill: Place the bank bill to be identified into the scanning area or scanner according to the specified direction and position.

[0073] Image Capture: Trigger a scan or image capture action and the system automatically or manually captures a high-resolution image of the receipt.

[0074] Image preprocessing includes: automatically adjusting image brightness and contrast, removing background noise, and correcting image tilt to ensure clear and readable images.

[0075] Apply image enhancement techniques, such as sharpening, smoothing, and binarization, to improve OCR recognition rate.

[0076] Text recognition includes: the OCR engine starts analyzing the image and identifying the text, numbers, barcode or QR code information on the bill.

[0077] Apply deep learning or machine learning models to improve the recognition accuracy of handwritten or unclear text.

[0078] Data verification and correction, including:

[0079] Data verification: The system automatically verifies the identified information, such as account number, amount, date, etc., to ensure the consistency and rationality of the data.

[0080] This includes comparison with existing records in the database to check the correctness of the information.

[0081] Manual correction: If there are doubts or errors in the recognition results, the system prompts the operator to make manual corrections.

[0082] The operator modifies the error information on the interface and saves it after confirming that it is correct.

[0083] Data storage and output

[0084] Data storage: Store the verified ticket information in the database for subsequent processing or retrieval.

[0085] Output and Reporting: The system generates an electronic report of the recognition results, including the original image, recognition data, and operation log. You can choose to print or send the report electronically to the relevant departments or individuals.

[0086] System maintenance and security

[0087] System maintenance: Regular system maintenance is performed, including software upgrades, hardware inspections, and data backups.

[0088] Monitor system performance to ensure recognition efficiency and data security.

[0089] Security measures: Implement strict data encryption and access control to prevent unauthorized access and data leakage.

[0090] End of process: When all bills have been processed, the operator exits the system and shuts down the equipment.

[0091] Beneficial effects:

[0092] Combining deep learning technology, a neural network model specifically for bill recognition was designed, which can accurately identify text, numbers, seals and other elements on bills.

[0093] Intelligent verification mechanism: The integrated intelligent verification function can automatically compare the consistency of bill information and effectively prevent data input errors.

[0094] User interface: Provides a friendly and intuitive operation interface, supports batch processing and real-time feedback of recognition results, and facilitates operator monitoring and management.

[0095] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A bank note recognition method, characterized in that: The identification method comprises: Acquiring an image and preprocessing the image to obtain a preprocessed image; Extracting and identifying features from the preprocessed image to obtain a recognition result; Performing intelligent verification and correction on the recognition result to obtain corrected data; The correction data is output and managed.

2. A bank note recognition method according to claim 1, characterized in that: The collecting of images and preprocessing of the images specifically include: Utilize a multi-angle camera system to capture receipts from different perspectives, combined with high-resolution scanning to ensure image clarity; The preprocessing stage includes image enhancement, denoising, and edge detection.

3. A bank note recognition method according to claim 1, characterized in that: Extracting and identifying features from the preprocessed image to obtain a recognition result specifically includes: A deep convolutional neural network (CNN) is used to extract features from preprocessed images and combined with a specific recognition model to identify key information of the bill.

4. A bank note recognition method according to claim 3, characterized in that: The key information for identifying bills specifically includes: Use the OCR engine to analyze the image and recognize the text, numbers, barcodes or QR codes on the bill.

5. A bank note recognition method according to claim 1, characterized in that: The intelligent verification and correction of the recognition result to obtain the correction data specifically includes: A rule-based verification algorithm is introduced to check the logic and consistency of the recognition results, and errors are corrected through human-computer interaction.

6. A bank note recognition method according to claim 1, characterized in that: The outputting and managing the corrected data specifically includes: The identification results are automatically input into the bank system, and a detailed identification report is generated to facilitate subsequent data management and auditing.

7. A bank note recognition method according to claim 6, characterized in that: The generated detailed recognition report specifically includes: original image, recognition data and operation log.

8. A bank bill recognition system, applying a bank bill recognition method according to any one of claims 1 to 7, characterized in that: The identification system comprises: An image preprocessing module is used to collect images and preprocess the images to obtain preprocessed images; A feature extraction and recognition module is used to extract and recognize features from the pre-processed image to obtain a recognition result; A verification and correction module, used to perform intelligent verification and correction on the recognition result to obtain correction data; The data output module is used to output and manage the correction data.

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