Card and Certificate Recognition Technology and Its Applications in HarmonyOS Next

This article aims to deeply explore the card and certificate recognition technology in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on practical development practices. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Overview of Card and Certificate Recognition Technology and Requirement Analysis

(1) Introduction to Technical Key Points

1. Key Points of Image Acquisition In the card and certificate recognition system of HarmonyOS Next, image acquisition is the first crucial step. To obtain clear and complete card and certificate images, multiple factors need to be considered. The resolution and focusing ability of the camera are of vital importance. A high-resolution camera can capture more detailed information of the card and certificate, and accurate focusing ensures the clarity of the image. For example, when recognizing an identity card, details such as the text, photo, and anti-counterfeiting marks on the identity card need to be clearly distinguishable; otherwise, it will affect the subsequent information extraction and recognition. At the same time, the shooting angle and distance also need to be reasonably controlled to ensure that the card and certificate are in an appropriate proportion in the image, avoiding deformation or the loss of some information. For example, when shooting a vehicle license, it is necessary to ensure that the entire vehicle license and key information (such as the license plate number, vehicle type, etc.) can be completely presented in the image.
2. Process of Information Extraction and Structured Recognition After the image acquisition is completed, the next step is the process of information extraction and structured recognition. This process mainly uses optical character recognition (OCR) technology to recognize and extract the text information in the card and certificate image. For an identity card, key information such as the name, gender, ethnicity, date of birth, address, and identity card number needs to be extracted; for a vehicle license, information such as the license plate number, vehicle type, owner's name, address, engine number, and vehicle identification number needs to be extracted. During the recognition process, first, preprocess the image, such as operations like grayscale conversion, noise reduction, and binarization, to improve the contrast between the text and the background, making it easier for subsequent character segmentation and recognition. Then, use a character segmentation algorithm to divide the text into individual characters or character blocks, and then use a deep learning model or traditional character recognition algorithm to classify and recognize them. Finally, assemble the recognized characters in a structured manner according to the structure of the card and certificate to form complete card and certificate information.

(2) Analysis of Application Requirements

1. Requirements in Identity Verification Scenarios Among numerous application scenarios, identity verification is one of the important applications of card and certificate recognition. For example, in scenarios such as opening an account in a financial institution, checking into a hotel, and airport security check, it is necessary to quickly and accurately recognize the customer's identity information to ensure the security and compliance of business processing. Through the card and certificate recognition technology of HarmonyOS Next, it is possible to automatically read the information of identity cards and other cards and certificates, and compare and verify it with the relevant database, which greatly improves the efficiency and accuracy of identity verification and reduces the cumbersome nature and error risks of manual operations. At the same time, combined with face liveness detection technology, the security of identity verification can be further enhanced to prevent others from using forged cards and certificates for identity fraud.
2. Requirements in Business Processing Scenarios In the business processing process, card and certificate recognition technology can achieve the rapid entry of information and improve the speed of business processing. Take the example of handling a loan business in a bank. Customers need to provide various card and certificate materials such as identity cards, vehicle licenses, and property ownership certificates. The traditional manual entry method is not only inefficient but also prone to errors. By using card and certificate recognition technology, bank staff only need to scan the cards and certificates with a device equipped with the HarmonyOS Next system, and the system can automatically extract and enter the relevant information, greatly shortening the business processing time and improving the customer experience. In addition, in the human resources management of an enterprise, when new employees join, they need to submit various certificates. Card and certificate recognition technology can achieve the rapid collection of certificate information and the establishment of employee files, improving the efficiency of human resources management.

(3) Comparison of Different Card and Certificate Recognition Technologies

1. Traditional Template Matching Recognition Technology The advantage of traditional template matching recognition technology is that the algorithm is relatively simple and has low requirements for computing resources, making it suitable for some scenarios where the requirement for recognition accuracy is not high and computing resources are limited. For example, in some simple access control systems, only the employee cards in a specific format need to be recognized, and the template matching technology can quickly determine whether the card and certificate match the preset template. However, its limitations are also obvious. It has poor adaptability to changes in the format and content of the card and certificate. Once the style of the card and certificate changes, such as changes in the font and layout, a new template needs to be made, resulting in high maintenance costs. Moreover, when dealing with cards and certificates with complex backgrounds, blurred images, or partial occlusions, the recognition accuracy will drop significantly.
2. Card and Certificate Recognition Technology Based on Deep Learning The card and certificate recognition technology based on deep learning has powerful learning ability and generalization ability, and can automatically learn the feature representation of cards and certificates, and has a good recognition effect on cards and certificates of different styles and different degrees of clarity. For example, when recognizing various versions of identity cards and vehicle licenses, the deep learning model can accurately extract the information in them, even if the cards and certificates have a certain degree of wear and tear, stains, or uneven lighting. At the same time, with the increase of training data and the optimization of the model, its recognition accuracy can be continuously improved. However, this technology has high requirements for computing resources and requires powerful hardware acceleration such as GPUs or TPUs to train and run the model quickly. Moreover, a large amount of annotated data is required for model training. If the data is insufficient or the annotation is inaccurate, it will affect the performance of the model.

II. Implementation of Card and Certificate Recognition Functions and Application Cases

(1) Common Implementation Methods of Card and Certificate Recognition and Code Examples (if applicable)

Although the specific card and certificate recognition development library is not clearly mentioned in the document, we can assume that there is a similar function library (similar to Tesseract OCR on other platforms). The following is a simplified conceptual code example to show the basic process of identity card recognition (assuming libraries and functions):

import { CardRecognitionLibrary } from '@ohos.cardrecognition';

// Load the identity card image (assuming the image file path has been obtained)
let idCardImagePath = 'id_card.jpg';
let idCardImage = CardRecognitionLibrary.loadImage(idCardImagePath);

// Image preprocessing (assuming the library provides corresponding preprocessing functions)
let preprocessedImage = CardRecognitionLibrary.preprocessImage(idCardImage);

// Identity card information extraction
let idCardInfo = CardRecognitionLibrary.extractIdCardInfo(preprocessedImage);

console.log('Name on the identity card:', idCardInfo.name);
console.log('Identity card number:', idCardInfo.idNumber);
// The output of other information is omitted

In this example, first, the identity card image is loaded, then the image is preprocessed, and finally, the key information on the identity card is extracted and output. In actual development, detailed parameter settings and function calls need to be made according to the specific library and API used to achieve accurate card and certificate recognition functions.

(2) Demonstration of Practical Application Cases

1. Case of Hotel Check-in Registration In the hotel check-in registration scenario, the front desk staff uses a device equipped with the HarmonyOS Next system to scan and recognize the guest's identity card. The guest only needs to place the identity card in the scanning area of the device, and the system automatically activates the card and certificate recognition function. First, the camera of the device collects the identity card image, and then quickly extracts the name, gender, identity card number, and other information on the identity card through the card and certificate recognition technology, and automatically fills it into the hotel's check-in registration system. At the same time, combined with face liveness detection technology, the guest's identity is verified to ensure that the check-in person is consistent with the identity card information. The whole process is fast and accurate, greatly shortening the guest's waiting time and improving the hotel's service efficiency and customer satisfaction.
2. Case in Vehicle Management Scenarios In vehicle management scenarios such as parking lot management or vehicle annual inspection, the card and certificate recognition technology of HarmonyOS Next also plays an important role. Take parking lot management as an example. When a vehicle enters the parking lot, the administrator uses a handheld device to scan and recognize the vehicle's license. The system automatically extracts information such as the license plate number and vehicle type on the vehicle license and compares it with the vehicle information in the parking lot management system. If the vehicle information matches and is valid, the barrier gate is automatically opened to allow the vehicle to pass; if the vehicle is not registered or there are abnormal situations, the system issues an alarm to prompt the administrator to deal with it. This automated card and certificate recognition and management method improves the management efficiency of the parking lot and reduces the errors of manual operations and the situation of vehicle congestion.

(3) Evaluation of Accuracy and Efficiency and Analysis of Influencing Factors

1. Evaluation of Accuracy and Influencing Factors The accuracy of card and certificate recognition can be evaluated by comparing it with the information manually entered. In practical applications, a certain number of card and certificate samples of different types and qualities are selected for testing, and the proportion of the number of correctly recognized information to the total number of information is calculated as the accuracy. There are many factors affecting the accuracy, and the quality of the card and certificate image is one of the key factors. If the image is blurry, the lighting is uneven, there are shadows or stains, etc., it will affect the clarity of the text, increase the recognition difficulty, and reduce the accuracy. For example, for an identity card photographed in a dimly lit environment, the text may be blurry, resulting in some information being misrecognized. The degree of wear and tear of the card and certificate also affects the accuracy. A severely worn card and certificate may cause some text to be missing or difficult to identify. In addition, if the layout changes of the card and certificate (such as the update of the identity card) are not updated in the recognition model in a timely manner, it may also lead to recognition errors.
2. Evaluation of Efficiency and Influencing Factors The efficiency evaluation mainly focuses on the speed of card and certificate recognition, that is, the time spent from image acquisition to the completion of information extraction and structured recognition. The main factors affecting the efficiency include the hardware performance of the device, the complexity of the algorithm, and the size and resolution of the image. A device with high performance can process image data more quickly. For example, a device equipped with a fast CPU and sufficient memory can accelerate the preprocessing and recognition process of the image. The complexity of the algorithm also affects the recognition speed. Although complex deep learning algorithms have high accuracy, the calculation time may be longer. Larger-sized and higher-resolution images require more computing resources and time to process. Therefore, in practical applications, appropriate image acquisition parameters need to be selected according to the performance of the device and the needs of the application scenario to balance the recognition accuracy and efficiency.

III. Optimization and Expansion Considerations of Card and Certificate Recognition Technology

(1) Proposed Optimization Methods

1. Improve Image Preprocessing Algorithms In the image preprocessing stage, more advanced image enhancement technologies can be adopted, such as the image denoising method based on deep learning, which can effectively remove the noise interference in the image and better retain the detailed features of the text. For the problem of uneven lighting, an adaptive lighting compensation algorithm can be adopted to dynamically adjust the brightness and contrast according to the local lighting situation of the image, making the text clearer and more distinguishable. For example, when processing a card and certificate image taken under strong light, the adaptive lighting compensation algorithm can reduce the brightness of the strong light area and increase the brightness of the shadow area, making the lighting of the entire card and certificate image more uniform. In addition, optimize the character segmentation algorithm and adopt a semantic segmentation method based on deep learning, which can more accurately segment the text area on the card and certificate, reduce character segmentation errors, and improve the subsequent recognition accuracy.
2. Optimize the Recognition Model For the optimization of the recognition model, more advanced deep learning architectures can be adopted, such as introducing Transformer-based models or convolutional neural network models combined with the attention mechanism. These models can better process the text sequence information on the card and certificate and improve the adaptability to different fonts, font sizes, and layouts. At the same time, use model compression technologies, such as pruning and quantization methods, to reduce the size of the model without significantly reducing the recognition accuracy, reduce the requirements for hardware resources, and improve the operation efficiency of the model on HarmonyOS Next devices. In addition, by increasing the diversity of training data, collecting more card and certificate samples of different types, different versions, and different qualities for training, the model can learn a wider range of card and certificate features and improve the generalization ability of recognition.

(2) Discussion on Expansion Application Directions

1. Integration with Intelligent Security Systems Card and certificate recognition technology can be deeply integrated with the intelligent security system of HarmonyOS Next. In security scenarios such as community access control and the entrance of an enterprise park, in addition to face liveness detection, the cards and certificates (such as access cards, work permits, etc.) carried by personnel are also recognized at the same time. By associating the card and certificate information with the personnel identity database, more accurate personnel identity verification and permission management can be achieved. For example, in an enterprise park, when an employee enters the park, the system automatically recognizes the information on their work permit and, combined with face liveness detection, ensures that the employee's identity is real and valid, and automatically opens the access control of the corresponding area according to the employee's position and permissions, improving the intelligence level and security of the security system.
2. Combination with Mobile Payment Scenarios In the field of mobile payment, card and certificate recognition technology also has potential application value. For example, in some mobile payment scenarios that require real-name authentication, such as opening a large-amount payment function and applying for financial services, users can take pictures of identity cards and other cards and certificates, and the system automatically recognizes and verifies the identity information without the need for manual entry, improving the convenience and security of payment. At the same time, combined with biometric technologies (such as fingerprint recognition and face recognition), multi-modal identity verification can be achieved, further ensuring the security of mobile payment.

(3) Experience Summary and Precautions

1. Experience in Data Security and Privacy Protection In the development process of card and certificate recognition, data security and privacy protection are of vital importance. Ensure that the transmission process of card and certificate images and recognition information is encrypted to prevent the information from being stolen during network transmission. On the device side, store the card and certificate images and recognition results securely and use encryption technology to protect the confidentiality of the data. For example, use encryption algorithms such as AES to encrypt the card and certificate information stored on the local device. At the same time, strictly abide by relevant laws, regulations, and privacy policies, clearly inform users of the purpose and scope of the use of card and certificate information, and process the data with the user's explicit consent to protect the user's right to privacy.
2. Precautions for Model Update and Maintenance With the update of the layout of cards and certificates, the improvement of anti-counterfeiting technology, and the emergence of new types of cards and certificates, it is necessary to update and maintain the recognition model in a timely manner. Establish an effective model update mechanism, regularly collect new card and certificate sample data, and retrain or fine-tune the model to ensure that the model can adapt to the new features of cards and certificates. At the same time, continuously monitor the performance of the model, and promptly discover and solve problems that occur in the practical application of the model, such as a decrease in accuracy and a slowdown in recognition speed. During the model update process, pay attention to the consistency and compatibility of the data to avoid situations where old versions of cards and certificates cannot be recognized or misrecognized due to model updates. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the card and certificate recognition technology in HarmonyOS Next and can better apply this technology in practical development to provide strong support for the digital transformation of various industries. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!