Bank Card Number Research Articles

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.

Video-Based Content Recognition of Bank Cards with Mobile Devices

In this paper, we propose an algorithm for detection and recognition of all information fields at the bank card front side based on video sequences. The algorithm is intended for use on mobile devices. This algorithm consists of the following basic steps: detection of the card boundaries in a frame, segmenting the information fields, improving the quality of segments, localizing the boundaries of symbols, and recognizing blocks of symbols. We also conduct a series of experiments. Experimental results show that our algorithm can achieve higher detection rates of 88% for all information fields and 92.5% for the bank card number and expiration date. The processing time per frame at different resolutions for each step by using iPhone 7 is presented. The experimental results confirm the efficiency of the proposed approach.

基于深度学习的银行卡号识别研究

基于深度学习的银行卡号识别研究

An Automatic Recognition Method for Bank Card Number

With the development of mobile internet, mobile payment has become one of the most popular payment methods. In order to improve work efficiency, reduce labor costs and enhance user experience, the intelligent identification of bank cards is widely used in mobile payment. Conventional optical character recognition (OCR) technology has the problems of low recognition rate when dealing with bank card text with complex background. Thus, a bank card number identification method based on deep learning is proposed. Firstly, the data set is expanded. Then the CRNN algorithms is used and optimized to identify the card number. Some experimental results show that the method have a high recognition rate.

Characteristics of Credit Instruments Issued by Stokvels in South Africa

Access to formal credit and other financial instruments remains a challenge for the majority of households in South Africa. The objective of this study was, therefore, to determine the characteristics of credit instruments issued by stokvels to households in South Africa. Prior studies have generally focused on mobilization of savings through stokvels while none has paid particular attention to the credit supply function of stokvels. This study attempts to fill this gap by using a self-administered research questionnaire on a sample of 386 respondents. Members of stokvels were surveyed from the cities of Pretoria and Johannesburg in the Gauteng province of South Africa. Data was analysed using descriptive statistics, exploratory factor and correlation analyses. Results show that stokvels issue short-term loans from less than 3 to 6 months. Interest rates are high ranging from 25% to 35% and are charged monthly. Loan sizes are small with approximately two-thirds of the respondents receiving loans above R500 while the remaining third received less than R500. Finally, all loans are secured by the borrower’s identification document or bank card and personal identification number. The results of this study have policy implications for financial institutions in South Africa.

Characteristics of Credit Instruments Issued by Stokvels in South Africa

Access to formal credit and other financial instruments remains a challenge for the majority of households in South Africa. The objective of this study was, therefore, to determine the characteristics of credit instruments issued by stokvels to households in South Africa. Prior studies have generally focused on mobilization of savings through stokvels while none has paid particular attention to the credit supply function of stokvels. This study attempts to fill this gap by using a self-administered research questionnaire on a sample of 386 respondents. Members of stokvels were surveyed from the cities of Pretoria and Johannesburg in the Gauteng province of South Africa. Data was analysed using descriptive statistics, exploratory factor and correlation analyses. Results show that stokvels issue short-term loans from less than 3 to 6 months. Interest rates are high ranging from 25% to 35% and are charged monthly. Loan sizes are small with approximately two-thirds of the respondents receiving loans above R500 while the remaining third received less than R500. Finally, all loans are secured by the borrower’s identification document or bank card and personal identification number. The results of this study have policy implications for financial institutions in South Africa.

基于数字图像处理的银行卡号智能识别技术

数字识别系统是现代化智能管理领域的重要组成部分，其作用特别体现在日常生活的智能化和数字化方面。本论文以我国各银行的银行卡为研究对象，提出了一种基于数字图像处理的银行卡号自动识别技术。该技术的主要目的是实现银行卡号信息的自动提取并将卡号以文本文件形式输出，以切合实际应用。其主要组成部分为：图像采集、图像预处理、数字号码定位、字符特征提取、字符标记、字符分割、字符识别等。通过实验测试，发现该技术可以实现85.6%以上的准确识别率。 The digital recognition system is an important part of the modern intelligent management field. Its role is particularly reflected in the intelligence and digital aspects of daily life. With the bank card of China’s Banks as the research object, this paper proposed a bank card number automatic identification technology based on digital image processing. The main purpose of this technology is to achieve the automatic extraction of bank card number information as a text file output for practical applications. Its main components are image acquisition, image preprocessing, digital number positioning, character feature extraction, character recognition, character segmentation and character recognition, etc. Through the experimental test, it is found that the technology can achieve more than 85.6% accuracy.

The holy grail of authentication

Two-factor authentication (2FA) has been about for much longer than you think. For a decade or more we have been used to being issued with a card reader (in essence a hardware token device) to use with our bank card and Personal Identification Number (PIN) when looking to complete our Internet banking transactions. And 2FA technology has, over the past year or so, been employed by seven of the 10 largest social networking sites (including Facebook, Twitter and LinkedIn) as their authentication measure of choice.1