Card Images Research Articles

Improving the Quality of Digital Images on Identity Cards Using Contrast Stretching and Retinex Methods

Enhancing the image quality of the Indonesian Identity Card is crucial to ensure the clarity and accuracy of information in various applications, such as identity verification, administration, and security. This study aims to improve the digital image quality of Identity Card using the Contrast Stretching and Retinex methods. Contrast Stretching is employed to enhance image contrast, while the Retinex method is used to maintain and improve the clarity of colors and image details. The implementation of these methods is carried out using Python. The test results show that these methods are effective in improving the quality of Identity Card images, making the information on the images clearer and easier to read. This research contributes to the development of more advanced image processing technology, which can be applied to various verification and administrative needs that require high reliability.

A custom-built deep learning approach for text extraction from identity card images

<span>Information found on an identity card is needed for different essential tasks and manually extracting this information is time consuming, resource exhaustive and may be prone to human error. In this study, an optical character recognition (OCR) approach using deep learning techniques is proposed to automatically extract text related information from the image of an identity card in view of developing an automated client onboarding system. The OCR problem is divided into two main parts. Firstly, a custom-built image segmentation model, based on the U-net architecture, is used to <span>detect the location of the text to be extracted. Secondly, using the location of the identified text fields, a (CRNN) based on long short-term memory (LSTM) cells is trained to recognise the characters and build words. Experimental results, based on the national identity card of the Republic of Mauritius, demonstrate that our approach achieves higher accuracy compared to other studies. Our text detection module has an intersection over union (IOU) measure of 0.70 with a pixel accuracy of 98% for text detection and the text recognition module achieved a mean word recognition accuracy of around 97% on main fields of the identity card.</span></span>

Research on the design of automatic image processing function for intelligent face management system

Abstract Around the background of rapid development of intelligent technology, an efficient image processing system oriented to the smart management of human faces is focused on. The system is mainly developed towards high speed, high definition, high integration and reliability. The article investigates a novel automatic image processing algorithm, covering three key modules: automatic exposure control, color interpolation and chromaticity space conversion. The algorithm can process the output image of CMOS sensor in Bayer format in real time and adjust the image parameters to obtain a high-quality image. In terms of face recognition performance, the algorithm has a significant advantage in recognition speed compared with other algorithms, and the average recognition accuracy reaches 94.258%. In the practical application of ID card portrait processing, the image shows a more uniform grayscale distribution in the range of 5 to 255 after automatic color adjustment, and the color quality is significantly improved. Meanwhile, in the portrait enhancement experiments, the images obtained with this image automatic processing algorithm outperform the traditional ID card image processing methods regarding information entropy, mutual information, standard deviation and peak signal-to-noise ratio (PSNR).

노인의 인지기능 저하 예방을 위한 청각 집중력 게임 개발에 관한 연구

The purpose of this study is to develop an auditory concentration game to prevent cognitive decline in the elderly. Previous studies have identified that hearing loss affects cognitive function due to the process of aging, and it has been confirmed that it is necessary to prevent this. Accordingly, based on Keller's (2013) ARCS model, an auditory concentration game was developed to prevent cognitive decline in the elderly with advice from two board game developers and an education expert. The auditory concentration game is, first, a card image and sound source that consider the emotions of the elderly with the goal of maintaining and improving cognitive function of the elderly. Through the auditory concentration game, interest and motivation for the prevention of cognitive decline were induced through the auditory concentration game, and the will to prevent dementia was promoted to improve cognitive function. Second, the stage of game development was reorganized into ideas and concept planning → category and mechanism → game rule production → game design and component production → game test five-step procedure, and it was produced with images and sound source considering the emotions of the elderly. Third, the sound source is significant in that it can be recorded in consideration of the emotions of the elderly and enjoyed various game methods, and can be applied to the cognitive program of the elderly by adjusting the difficulty of the game. Based on this study, the following studies are needed in subsequent studies. First, since it was developed as an auditory concentration game to prevent cognitive decline in the elderly, it is necessary to analyze whether it has an effect on the improvement of cognitive function. Second, it is necessary to analyze the correlation or difference because it was targeted at the entire elderly regardless of gender. Third, it is necessary to develop a specific instructional plan that can be used for the cognitive program of the elderly, and it is expected to be used as a cognitive program for the elderly such as senior citizen centers after supplementing and modifying the currently developed auditory concentration game to produce a complete game.

Robust OCR Pipeline for Automated Digitization of Mother and Child Protection Cards in India

The Universal Immunization Programme in India has a mandate to fully vaccinate all of India’s 27 million children born annually. The vaccination doses are recorded by frontline health workers on standardized paper-based Mother and Child Protection (MCP) cards, which are manually digitized by data entry operators, resulting in poor data quality, delays, and significant time and resources. In our article, we focus on Optical Character Recognition– (OCR) based automated digitization of MCP card images captured through a smartphone application developed by us. By utilizing a standardized template for the MCP cards, which is available a priori , we register the card images and perform OCR on the extracted region of interest (ROIs). Since the cards with curvature or torn edges had poor ROIs, we built a global–local alignment technique that first approximates the ROI using global homography and then refines using a local homography resulting in improved accuracy. Our pipeline gives a character level accuracy of 98.73% on our dataset against 75.02% by Google Cloud Vision and 79.26% by Azure OCR. We also describe our field testing experience, where the digitized MCP card images were used to provide useful features on the smartphone application for health workers to conduct vaccination sessions.

Hybrid Nature-Inspired Optimization and Encryption-Based Watermarking for E-Healthcare

With the growth and popularity of the utilization of medical images in smart healthcare, the security of these images using watermarks is one of the most recent research topics. This algorithm is based on the joint use of dual watermarking, nature-inspired optimization, and encryption schemes utilizing redundant-discrete wavelet transform (RDWT) and randomized-singular value decomposition (RSVD). The key idea of the proposed method is to embed system encoded media access control (MAC) address in patient's ID card image via discrete wavelet transform (DWT) to generate the final mark. Afterward, embed the generated watermark into computed tomography (CT) scan images of the COVID-19 patient and general images through employing the RDWT and RSVD. Further, we use a hybrid of particle swarm optimization (PSO) and Firefly optimization techniques to determine the optimal scaling factor for embedding purposes. After that, the watermarked CT scan image is encrypted using an encryption technique based on a nonlinear-chaotic map, random permutation, and singular value decomposition (SVD). Extensive evaluations establish the benefit of our proposed algorithm over the traditional schemes. The optimal robustness is more effective than the five traditional schemes at lower computational efficiency.

PAN Card Fraud Detection Using Machine Learning: A Case Study

Artificial Intelligence research in the area of computer vision teaches machines to comprehend and interpret visual data. Machines can accurately identify and classify objects using digital images from cameras and videos, deep learning models, and then respond to what they “see”. The detection of document manipulation is an area where these technologies are crucial. Everyone in India is given a PAN (Permanent Account Number), which is a 10-character alphanumeric code, especially those who pay taxes. Various organizations make use of identity documents like PAN cards. The PAN Card given by the employee or client may be genuine or fraudulent. This review paper’s main goal is to use computer vision to determine whether the provided PAN card image is authentic or altered (fake). Our goal is to create a method that uses machine learning techniques to categorize PAN cards. Keywords: Machine Learning, Convolutional Neural Network, Fraud, Image Processing

명함 이미지 회전 판단을 위한 딥러닝 모델 비교

고객이 온라인으로 요청한 명함을 자동으로 명함을 인쇄하는 스마트 명함 인쇄 시스템이 활성화되고 있다. 이때, 문제는 고객이 시스템에 제출한 명함이 비정상일 수 있다는 것이다. 본 논문에서는 인공 지능 기술을 도입하여 명함의 이미지가 비정상적으로 회전됐는지 여부를 판정하는 문제를 다룬다. 명함은 0도, 90도, 180도, 270도 회전한다고 가정하였다. 특별한 인공신경망을 설계하지 않고 기존의 VGG, ResNet, DenseNet 인공신경망을 적용하여 실험하였는데 모든 신경망이 97% 정도의 정확도로 이미지 회전을 분별할 수 있었다. DenseNet161은 97.9%의 정확도를 보였고 ResNet34도 97.2%의 정밀도를 보였다. 이는 문제가 단순할 경우, 복잡한 인공신경망이 아니어도 충분히 좋은 결과를 낼 수 있음을 시사한다.

Synthetic ID Card Image Generation for Improving Presentation Attack Detection

Currently, it is ever more common to access online services for activities which formerly required physical attendance. From banking operations to visa applications, a significant number of processes have been digitised, especially since the advent of the COVID-19 pandemic, requiring remote biometric authentication of the user. On the downside, some subjects intend to interfere with the normal operation of remote systems for personal profit by using fake identity documents, such as passports and ID cards. Deep learning solutions to detect such frauds have been presented in the literature. However, due to privacy concerns and the sensitive nature of personal identity documents, developing a dataset with the necessary number of examples for training deep neural networks is challenging. This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks. These methods include computer vision algorithms and Generative Adversarial Networks. Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS.

Tarkib’s Contemporary Arts Festival in Baghdad

Tarkib’s Contemporary Arts Festival in Baghdad

Sticky Pi is a high-frequency smart trap that enables the study of insect circadian activity under natural conditions.

In the face of severe environmental crises that threaten insect biodiversity, new technologies are imperative to monitor both the identity and ecology of insect species. Traditionally, insect surveys rely on manual collection of traps, which provide abundance data but mask the large intra- and interday variations in insect activity, an important facet of their ecology. Although laboratory studies have shown that circadian processes are central to insects' biological functions, from feeding to reproduction, we lack the high-frequency monitoring tools to study insect circadian biology in the field. To address these issues, we developed the Sticky Pi, a novel, autonomous, open-source, insect trap that acquires images of sticky cards every 20 minutes. Using custom deep learning algorithms, we automatically and accurately scored where, when, and which insects were captured. First, we validated our device in controlled laboratory conditions with a classic chronobiological model organism, Drosophila melanogaster. Then, we deployed an array of Sticky Pis to the field to characterise the daily activity of an agricultural pest, Drosophila suzukii, and its parasitoid wasps. Finally, we demonstrate the wide scope of our smart trap by describing the sympatric arrangement of insect temporal niches in a community, without targeting particular taxa a priori. Together, the automatic identification and high sampling rate of our tool provide biologists with unique data that impacts research far beyond chronobiology, with applications to biodiversity monitoring and pest control as well as fundamental implications for phenology, behavioural ecology, and ecophysiology. We released the Sticky Pi project as an open community resource on https://doc.sticky-pi.com.

The Increase in the Ability to Speak through the Katumbar Media in Children Aged 3-4 Years in Kindergarten

Word cards pictorial of one of the media that can help the development of the ability to speak the child, because the media card images are concrete. From the observation of the researcher, the problem that often occurs in the KB Aisyiyah Bustanul Athfal 1 Candi. The ability to speak, children Play Groups yet can achieve optimal development, this is because the ability to speak the child is not smooth and less interestingly media used during the learning process takes place. Therefore it is necessary supporting media so that the kids want to reveal the experience of day - to- day, so that children become excited in following the learning and the goal of the teacher to improve the speaking skills of children can be successfully and running maximum by using the Card Word Picture. This research using the Method of Classroom Action Research to improve the ability to speak. This study was conducted in 2 cycles. The study was conducted in three stages namely pre-cycle, cycle I and cycle II to obtain the results as expected. In the pre cycle, most children still acquire an average value of 44,98% while in the first cycle the percentage value increases so that the obtained percentage value to be 63,31%, which means less than the maximum. Then the researchers proceeded to give the action in cycle II, after doing research on the second cycle is the percentage of the average value of the child becomes 81,66%, resulting from the acquisition of the average in cycle I and cycle II increased again. It can be seen from the comparison between cycle I and cycle II during the study. From the results obtained, there is an increase in proving that with the Word Cards Picture can improve the Speaking ability of the child.

Designing a Real-Time-Based Optical Character Recognition to Detect ID Cards

This research 0aims to design a Real-time ID card detection based on Optical Character Recognition (OCR). OCR detects and records information into CSV files using a camera. Hopefully, it can become one of the administrative solutions in Indonesia by using existing identity cards using OCR in real time. This research method was carried out independently in August 2021 using ID cards as objects. The tool involved was a 320x320 pixel webcam camera on an HP Intel Core i5 7th Gen notebook. The software used by Easy OCR was Pytorch-based. ID cards were detected using an algorithm by TensorFlow object detection with SSD MobileNet V2 FPNLite 320x320 as the pre-trained model of Tensorflow. The researchers collected ID card images using a webcam with various light conditions and orientations and label them using labeling. The researchers trained it with only 20 photos. After 3000 training steps, the researchers obtained about 0.17 loss and 0.95. Thus, the ID card detection tool using OCR runs well.

An intelligent threats solution for object detection and resource perspective rectification of distorted anomaly identification card images in cloud environments

An intelligent threats solution for object detection and resource perspective rectification of distorted anomaly identification card images in cloud environments

Generating personalized business card designs from images.

Rising competition in the retail and hospitality sectors, especially in densely populated and touristic destinations is a growing concern for many business owners, who wish to deliver their brand communication strategy to the target audience. Many of these businesses rely on word-of-mouth marketing, delivering business cards to customers. Furthermore, the lack of a dedicated marketing team and budget for brand image consolidation and design creation often limits the brand expansion capability. The purpose of this study is to propose a novel system prototype that can suggest personalized designs for business cards, based on an existing business card picture. Using perspective transformation, text extraction and colour reduction techniques, we were able to obtain features from the original business card image and generate an alternative design, personalized for the end user. We have successfully been able to generate customized business cards for different business types, with textual information and a custom colour palette matching the original submitted image. All of the system modules were demonstrated to have positive results for the test cases and the proposal answered the main research question. Further research and development is required to adapt the current system to other marketing printouts, such as flyers or posters.

An End-to-End Method to Extract Information from Vietnamese ID Card Images

Information extraction from ID cards plays an important role in many daily activities, such as legal, banking, insurance, or health services. However, in many developing countries, such as Vietnam, it is mostly carried out manually, which is time-consuming, tedious, and may be prone to errors. Therefore, in this paper, we propose an end-to-end method to extract information from Vietnamese ID card images. The proposed method contains three steps with four neural networks and two image processing techniques, including U-Net, VGG16, Contour detection, and Hough transformation to pre-process input card images, CRAFT, and Rebia neural network for Optical Character Recognition, and Levenshtein distance and regular expression to post-process extracted information. In addition, a dataset, including 3.256 Vietnamese ID cards, 400k manual annotated text, and more than 500k synthetic text, was built for verifying our methods. The results of an empirical experiment conducted on our self-collected dataset indicate that the proposed method achieves a high accuracy of 94%, 99.5%, and 98.3% for card segmentation, classification, and text recognition.

Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra

Blood type in the medical world can be divided into 4 groups, namely A, B, AB and O. To be able to find out the blood type, a blood type test must be done. So far, human blood type detection is still done manually to observe the agglutination process. This research applies a blood type identification process using image processing. This system works by reading the blood type card image that has been filled with blood samples, then it will be processed through a histogram process to get the minimum and maximum RGB values and pixel locations which are then classified by Artificial Neural Networks (ANN) to determine the blood type from the training results and data matching. From the test results using 12 samples, it was found that the average error in blood type identification was 16.67%.

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

Preventing face morphing attacks by using legacy face images

Countries allow citizens to upload a face image or provide printed copies to authorities to issue their passport. This allows prior image manipulation with criminal intent. A composite image can be created by blending the images of two individuals before submitting the composite image to the authorities. Depending on several factors, the submitted morphed face image can fool the issuing officer to issue a legitimate document. The document can then be successfully used by either contributor to attack the automatic Face Recognition Systems (FRS) operating, for example, at Automatic Border Control (ABC) airport gates. This is known as a Morphing Attack (MA), an identity sharing scheme with serious consequences. Here, the security vulnerabilities due to MAs are identified and analysed, and an additional security measure that allows mitigating the risk or preventing MAs in certain scenarios is proposed. The measure introduces more comparisons by keeping the old passport or ID card image in the chip, in passport renewal applications or first time passport applications, respectively. This approach is implemented with two FRSs on a challenging dataset and the dramatic decrease in the vulnerability is shown. Finally, their performance is compared with a state-of-the-art MA detection algorithm on the same dataset.

The Method of Recognizing Bank Card Number Based on MATLAB

In this paper, a quick recognition method of bank card number based on MATLAB is designed, which can identify bank card number automatically. Through the gray processing, edge cutting processing, card number positioning processing, template matching card number recognition and other processing of the bank card image, an accurate and clear bank card number is obtained, which has a certain use value in e-commerce and the Internet. it also provides some reference for image processing and character recognition.