Bank Checks Research Articles

Advanced Algorithms for Offline Signature Recognition and Enhanced Forgery Detection Mechanisms

In contemporary society, signatures hold significant importance in various critical documents such as bank cheques, passports, and driver's licenses. Unfortunately, they can be counterfeited through various means, giving rise to issues like fraudulent identifications, identity theft, and cyber-attacks. To mitigate this problem, our project is centered around the development of a system that discerns the authenticity of a signature, distinguishing between genuine and forged signatures within a dataset. We have employed Convolutional Neural Networks (CNN) and deep learning for this purpose. This choice is driven by the understanding that signatures evolve over time due to a range of behavioral factors like age, mental state, and physical well-being. Consequently, our system is designed to adapt and learn from diverse training datasets, enhancing its accuracy in detection. While online and offline signature verification methods exist, our project primarily focuses on the latter, specifically targeting the detectionof forged offline signatures.

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION

Abstract—Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioural features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

Enhancing Signature Forgery Detection System Using CNN-SVM

Handwritten signatures are widely used as a means of personal identification and authentication. Many documents like bank cheques and legal transactions require signature verification. But considering a large number of documents, it is a very difficult and time-consuming task. Therefore, ensuring the necessity for a robust automatic signature verification tool that aims to reduce fraud in all related financial transaction sectors. The current visual verification depends mainly on the experience, mood, and working environment of the verifier which ultimately wastes both time and money. Moreover, it is difficult for the eyes of any experts to precisely verify the ratios between lines and angles of a genuine signature to a fraud signature. Therefore, we propose an automatic signature verification technique using the recent advances in image processing and machine learning.

Comparing Traditional and Digital Methods for Detecting Forged Signatures in Forensic Analysis

Abstract: Signatures are considered proofs for the validation of documents. Forgery of the signature is seen in almost all documents, ranging from bank checks, property papers, legal documents, and so on. The analysis of the signature can be a challenging task for forensic experts as fewer alphabets are used, so finding characteristics is difficult. In this research paper, we have discussed two ways of analyzing forged signatures: traditional and digital methods (Matlab and Python software). Traditional method: manual analysis of forged signatures with the help of a stereomicroscope and magnifying glass; digital method: using Matlab and Python software for analysis. In Matlab software, a curve fitting function is used in which the deviation of the graph is analyzed for both original and forged signatures. In Python software, two samples are added, one of which is the original signature and the other is the questioned sample. For analysis, 400 samples of signatures were used: 200 samples of the original signature and 200 samples of the questioned signature. The main aim of the paper is to find an easy and less time-consuming method for the analysis of the forged signature. The digital method has shown an accuracy of 95%. So it can be said that the digital method requires less time than the traditional method, and the accuracy and efficiency are almost the same.

Enhancing Digital Cheque Security: A Machine Learning Approach to Signature Fraud Mitigation

Enhancing Digital Cheque Security: A Machine Learning Approach to Signature Fraud Mitigation

Signature Fraud Detection Using Deep Learning

The use of signatures for personal identification and verification is quite common. Signatures are validated for many documents such as Bank cheques and legal transactions. The necessity for effective automated solutions for signature verification has grown as signatures are now a prerequisite for both authorization and authentication in legal activities. Two images—the original signature and the test signature—are used as input in this process. To determine whether the signature is fake or not, the characteristics that were extracted are compared, and the difference in error values between them is examined. The growing digital landscape has increased the need for robust and efficient fraud detection systems. This project presents a unique approach to detecting signature fraud using deep learning techniques, specifically employing Siamese Neural Networks, implemented in Python. With a dataset comprising 2149 signature images, encompassing both genuine and fraudulent samples from Dutch users, our model demonstrates remarkable accuracy. The Siamese Neural Network architecture excels in signature verification tasks by learning to distinguish between genuine and fraudulent signatures through contrastive learning. The key achievement of this project is the exceptional accuracy levels attained during the training and validation phases. The model's training accuracy stands at an impressive 98.00%, while the validation accuracy reaches an astonishing 99.00%. These high accuracy rates are a testament to the effectiveness of Siamese Neural Networks in signature fraud detection. In a world where the security of digital signatures is paramount, this project showcases the power of deep learning and Siamese Neural Networks in safeguarding against fraudulent activities. The model's success in accurately distinguishing between authentic and forged signatures offers promising potential for enhancing security measures in various domains, including finance, legal, and document management.

Enhancement of handwritten text recognition using AI-based hybrid approach

Handwritten text recognition (HTR) within computer vision and image processing stands as a prominent and challenging research domain, holding significant implications for diverse applications. Among these, it finds usefulness in reading bank checks, prescriptions, and deciphering characters on various forms. Optical character recognition (OCR) technology, specifically tailored for handwritten documents, plays a pivotal role in translating characters from a range of file formats, encompassing both word and image documents. Challenges in HTR encompass intricate layout designs, varied handwriting styles, limited datasets, and less accuracy achieved.Recent advancements in Deep Learning and Machine Learning algorithms, coupled with the vast repositories of unprocessed data, have propelled researchers to achieve remarkable progress in HTR. This paper aims to address the challenges in handwritten text recognition by proposing a hybrid approach. The primary objective is to enhance the accuracy of recognizing handwritten text from images. Through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a Connectionist Temporal Classification (CTC) decoder, the results indicate substantial improvement. The proposed hybrid model achieved an impressive 98.50% and 98.80% accuracy on the IAM and RIMES datasets, respectively. This underscores the potential and efficacy of the consecutive use of these advanced neural network architectures in enhancing handwritten text recognition accuracy.•The proposed method introduces a hybrid approach for handwritten text recognition, employing CNN and BiLSTM with CTC decoder.•Results showcase a remarkable accuracy improvement of 98.50% and 98.80% on IAM and RIMES datasets, emphasizing the potential of this model for enhanced accuracy in recognizing handwritten text from images.

Чеки как предмет контрабанды денежных инструментов

The Russian Federation is a member of the Customs Union, whose activities are aimed at more effective realization of the common benefits and national interests of its members. In the context of this union, conditions are being created between the participants for deepening economic integration, developing cooperation in the monetary and financial sphere, ensuring the free movement of goods, services and capital, increasing the role of national currencies in foreign trade operations and conducting a coordinated monetary policy. Consequently, within the framework of established contractual relations, compliance with the requirements of currency control and currency regulation contributes to the development of effective ways to influence public relations that are developing regarding the movement of capital. At the same time, statistical data confirm the stability of high rates of damage from smuggling of funds and (or) monetary instruments. The reasons for such indicators are seen in the problems that arise when interpreting the distinctive features of the crime in question, primarily the subject. The article is devoted to the consideration of traveler’s checks and checks (bank checks) as an object of smuggling of monetary instruments.

Data Processing from Bank Cheques by Utilising Split Attribute Character Analysis and Multi Branch Network Forest Classifier Techniques

Bank cheques are primarily used for conducting financial transactions, resulting in their substantial daily processing volumes worldwide. The automation of the whole process of recognising and verifying cheques has the potential to significantly reduce both the time and expenditures associated with cheque execution. The field of automatic bank cheque processing system is now gaining prominence in the realm of computer vision, image processing, pattern recognition, machine learning, and deep learning. The study places particular emphasis on the sequential processes involved in the automated bank Cheque processing system, including picture capture, pre-processing, and extraction and identification. This article provides an overview of the sequential processes included in the automated data extraction system. This research aims to propose strategies for the automated processing of bank cheque images via the use of Split Attribute character analysis and Multi branch network forest classifier. The study indicates that the recommended technique demonstrates satisfactory performance by achieving high levels of accuracy, precision, recall, and F score.

OCR Using Python and Its Application

Optical Character Recognition (OCR) of papers has tremendous practical value given the prevalence of handwritten documents in human exchanges. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analysable data. In the past ten years, academics have developed systems that automatically evaluate printed and handwritten documents to convert them to electronic format. In the modern era, as demand for computer systems arose, the demand to convert paper text and computer vision also erose. To interact the computer with capability to read text from images, videos and images have been arose rapidly and many software companies came in role to fulfil this need. One of the active and difficult study areas in the world of pattern recognition and image processing has been handwriting recognition. Among its many uses are bank checks, reading assistance for the blind, and the conversion of any handwritten document into structural text. The main aim of this paper is to create a searchable pdf from the image and bring the application to easy use and deployable on premises and cloud.

Comprehensive Analysis of Indian Bank Cheques for their Security Features and Forensic Authentication

Abstract: Background: Security features play a pivotal role in authenticating various security documents, particularly financial instruments like cheques. In addition to safeguarding the identity of the holder, it is of utmost importance to shield these documents from forgery attempts. To achieve this goal effectively, the implementation of security measures such as watermarks, unique printing techniques, ultraviolet-visible features, VOID-Pantograph, Micro-Printing, and other relevant methods becomes imperative. CTS-2010 lays down some really important guidelines with respect to the use of security features in Indian cheques with an attempt to standardize it with the ever-changing technology along with the Modus-Operandi of the Criminal. Results: The current research paper conducts a comparative analysis of the security features being currently utilized in cheques across nine different banks in India, with the help of the VSC-8000 H/S system. Cheques adhering to the CTS-2010 standards share common dimensions. In terms of security, all reviewed cheques have watermarks with the bank's name and logo, often accompanied by "CTS-INDIA” along with UV Fluorescence Ink is used for bank logos and various elements like amount in words, emitting distinct colors under UV light. Unique micro lettering is present under the pay column, rupees column, and amount box. Notably, SBI, HDFC, and Union Bank cheques exhibit the highest number of security features, enhancing their security value. Conclusions: By meticulously identifying and scrutinizing the presence or absence of covert features in these cheques, the study emphasizes the significance of incorporating as many of these measures as possible to enhance the overall security value of cheques on a universal scale. These findings are crucial for banks and financial institutions, guiding them in fraud prevention and customer safeguarding. Through strong security measures and ongoing advancements in design, printing, and technology, banks can effectively reduce risks linked to counterfeit cheques and unauthorized modifications.

Offline Handwritten Chinese Character Using Convolutional Neural Network: State-of-the-Art Methods

Given the presence of handwritten documents in human transactions, including email sorting, bank checks, and automating procedures, handwritten characters recognition (HCR) of documents has been invaluable to society. Handwritten Chinese characters (HCC) can be divided into offline and online categories. Online HCC recognition (HCCR) involves the trajectory movement of the pen tip for expressing linguistic content. In contrast, offline HCCR involves analyzing and categorizing the sample binary or grayscale images of characters. As recognition technology develops, academics’ interest in Chinese character recognition has continuously increased, as it significantly affects social and economic development. Recent development in this area is promising. However, the recognition accuracy of offline HCCR is still a sophisticated challenge owing to their complexity and variety of writing styles. With the advancement of deep learning, convolutional neural network (CNN)-based algorithms have demonstrated distinct benefits in offline HCCR and have achieved outstanding results. In this review, we aim to show the different HCCR methods for tackling the complexity and variability of offline HCC writing styles. This paper also reviews different activation functions used in offline HCCR and provides valuable assistance to new researchers in offline Chinese handwriting recognition by providing a succinct study of various methods for recognizing offline HCC.

Hand Written Digit Recognition using CNN

Abstract: Digit Recognition is an interesting and significant topic. Because the manually written digits are not all the same size, thickness, position, and direction, and to determine the issue of handwritten digit recognition, different obstacles must be considered. The variety and distinctiveness of different people's composition styles also influence the appearance and presence of the digits. It is a method of perceiving and organizing transcribed digits. It offers a wide range of uses, such as programmed bank checks, postal locations, and tax paperwork, among others. The goal of this project is to create a classification algorithm that can recognize handwritten digits. The outcomes of some of the most widely used Machine Learning Algorithms, such as SVM, KNN, and RFC, as well as Deep Learning calculations, such as multilayer CNN, using Keras, Theano, and TensorFlow. Using these, an accuracy of 98.70% was reached using CNN (Keras) when compared to 97.91% using SVM, 96.67% using KNN, and 96.89% using RFC.

Automated Bank Cheque Processing System

Abstract: While digital technology has advanced rapidly in recent years, many financial institutions such as banks still rely on conventional methods to process bank cheques manually. This process is time-consuming and can take several days for the transfer of funds to be completed as it involves verification by intermediaries. As a result, costs can be high. This proposed system aims to streamline the cheque processing process, reduce costs, and improve efficiency. This system will facilitate the process and lead to reduction in time and costs. When it comes to the clearance of bank cheques and monetary transactions, it should not only be dependable and robust, but it should also save time, which is a crucial element in nations with a large population. By automating the entire cheque processing workflow, banks can process cheques faster and more accurately, which improves customer satisfaction and reduces costs associated with manual processing. In this paper, we propose an automated system which extracts relevant details of a bank cheque like Payee Name, Amount in words and number, date, bank name, cheque number, IFSC code, using machine learning algorithms, Optical Character Recognition (OCR) to extract relevant data from the cheque image, deep learning and Convolutional Neural Networks (CNN) to verify that the extracted data matches the information on file, image segmentation to separate different components of the cheque, and image feature extraction to identify patterns and features in the cheque image that can help detect signs of fraud or forgery. It also uses feature extraction to verify the signature on the cheque with the existing signature stored in the database. Additionally, the combination of these methods has improved to reduce errors, making automated bank cheque processing utilizing machine learning a potent tool for banks wishing to automate their check processing procedures.

A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition.

Courtesy amount recognition from bank checks is an important application of pattern recognition. Although much progress has been made on isolated digit recognition for Indian digits, there is no work reported in the literature on courtesy amount recognition for Arabic checks using Indian digits. Arabic check courtesy amount recognition comes with its own unique challenges that are not seen in isolated digit recognition tasks and, accordingly, need specific approaches to deal with them. This paper presents an end-to-end system for courtesy amount recognition starting from check images as input to recognizing amounts as a sequence of digits. The system is a hybrid system, combining rule-based modules as well as machine learning modules. For the amount recognition system, both segmentation-based and segmentation-free approaches were investigated and compared. We evaluated our system on the CENPARMI dataset of real bank checks in Arabic. We achieve 67.4% accuracy at the amount level and 87.15% accuracy at the digit level on the test set consisting of 626 check images. The results are presented with detailed analysis, and some possible future work is identified. This work can be used as a baseline to benchmark future research in Arabic check courtesy amount recognition.

A Siamese Network based Writer Independent Offline Signature Verification

Signatures are frequently used for both personal authorization and verification. The signatures on numerous papers, including legal agreements and bank checks, must be verified. Offline signature verification is a type of authentication that examines the physical action of signing while measuring the characteristics of an user's handwriting. In the present paper, an offline signature verification method was developed utilising Convolutional Siamese Network of Deep Learning [4]. Convolutional Siamese networks, which are dual networks that shares parameters, can be developed to acquire a feature map. Thus, Convolutional Siamese network is employed to verify the person's signature to a input signature that is saved in the database. Research has been conducted with a dataset of signatures that includes 750 signatures from 30 different individuals. An Accuracy of 91.6% was attained by the proposed solution.

Handwritten Digit Prediction Using CNN

Abstract: For many years, numerous methods have been used in extensive research on handwriting recognition. The capacity to create an effective algorithm that can recognise handwritten digits given by users via scanner, tablet, and other digital devices is at the core of the issue. The automatic processing of bank checks, postal addresses, and other sorts of data already makes substantial use of handwritten digit recognition. Computational intelligence methods like artificial neural networks used by several current systems. CNN and the MNIST data set will be used to complete this. Handwriting Recognition, Deep Learning, CNN, and Computational Intelligence are key terms.

A Strategic Approach to Design Multi-Functional RGB Luminescent Security Pigment Based Golden Ink with Myriad Security Features to Curb Counterfeiting of Passport.

Authentication and verification of the most important government issued identity proof, i.e. passport has become more complex and challenging in the last few decades due to various innovations in ways of counterfeiting by fraudsters. Here, the aim is to provide more secured ink without altering its golden appearance in visible light. In this panorama, a novel advanced multi-functional luminescent security pigment (MLSP) based golden ink (MLSI) is developed that provides an optical authentication and information encryption features to protect the legitimacy of the passport. The advanced MLSP is derived from the ratiometric combination of different luminescent materials to form a single pigment which emits red (620 nm), green (523 nm) and blue (474 nm), when irradiated via 254, 365 and 980 nm NIR wavelengths, respectively. It also includes magnetic nanoparticles to generate magnetic character recognition feature. The MLSI has been fabricated to examine its printing feasibility and stability over different substrates using the conventional screen-printing technique against harsh chemicals and under different atmospheric conditions. Hence, these advantageous multi-level security features with golden appearance in visible light is a new breakthrough toward curbing the counterfeiting of passport as well as bank cheques, government documents, pharmaceuticals, military equipment, and many more.

Automatic imagery Bank Cheque data extraction based on machine learning approaches: a comprehensive survey

Automatic imagery Bank Cheque data extraction based on machine learning approaches: a comprehensive survey

Machine Learning–Based Profiling in Test Cheating Detection

AbstractIn recent years, machine learning (ML) techniques have received more attention in detecting aberrant test‐taking behaviors due to advantages when compared to traditional data forensics methods. However, defining “True Test Cheaters” is challenging—different than other fraud detection tasks such as flagging forged bank checks or credit card frauds, testing organizations are often lack of physical evidences to identify “True Test Cheaters” to train ML models. This study proposed a statistically defensible method of labeling “True Test Cheaters” in the data, demonstrated the effectiveness of using ML approaches to identify irregular statistical patterns in exam data, and established an analytical framework for evaluating and conducting real‐time ML‐based test data forensics. Classification accuracy and false negative/positive results are evaluated across different supervised‐ML techniques. The reliability and feasibility of operationally using this approach for an IT certification exam are evaluated using real data.