Bank Checks Research Articles

Revisión de algoritmos de verificación automática de firmas off-line

Nowadays, the signature is one of the most accepted badges for personal identification. Its inclusion is mandatory in documents such as bank checks, contracts, credit cards, among other public and private documents. However, the signature has become an attractive target for counterfeiting and, consequently, for fraud. For this reason, research has been carried out on automated signature recognition and state-of-the-art studies that are now required to be updated, since the most comprehensive work dates back to 2008 and in the following years further research has been carried out. The present work focuses on the comparative study of verification techniques of signatures offline from the point of view of efficiency and accuracy to verify the person’s authenticity. The research methodology used considers the procedure proposed by Kitchenham, which has been adapted, and involves the phases of planning, development and reporting of the review.

A novel database for automatic processing of Persian handwritten bank checks

This paper introduces a database of Persian handwritten bank checks. The database includes legal amounts, courtesy amounts, dates, receiver names, signatures and account numbers. In addition to checks, the database also comprises handwritten forms with words used in legal amounts, digits employed in the courtesy amounts and signatures of contributors. The database can be employed for evaluation of segmentation and recognition of different fields in checks as well as for verification of signatures. Data is collected and organized in two series. The first series, comprising 500 hand filled Persian checks and 500 forms contributed by 500 different individuals, supports evaluation of segmentation and recognition tasks. The second series contributed by 100 writers supports evaluation of signature verification systems and comprises 200 genuine checks, 100 forms and 200 forged checks. To provide an insight to other researchers, experiments have been carried out on recognition of dates and courtesy amounts with recognition rates of 76% and 73%, respectively. Likewise, experiments on signature verification with skilled forgeries realized an average error rate of 12.25%. The database, named Persian Handwriting Bank Checks (PHBC) Database, is freely available to the research community upon request to the authors. It is expected that the developed database will not only allow researchers working on automatic processing of Persian checks to evaluate their proposed techniques but will also serve to meaningfully compare different recognition and verification techniques.

IMPLEMENTASI ASAS DROIT DE PREFERENCE TERHADAP JAMINAN HAK TANGGUNGAN OLEH PIHAK PERBANKAN DALAM PERJANJIAN KREDIT

This study entitled Implementation of the principle of droit de preference to the guarantee of mortgage rights by the banks in the credit agreement. This study discusses the implementation of the principle of droit de preference on the guarantee of mortgage rights by the banks in credit agreements, the constraints found in the implementation of the principle of droit de preference on the guarantee of mortgage rights by the banks in credit agreements and how to overcome these obstacles.Implementation of the principle of droit de preference on the guarantee of mortgage by PT Bank Tabungan Pensiunan Nasional, Tbk in credit agreement Number 122 Credit Agreement in which to guarantee payment of all amount owed and obliged to be paid by debtor to bank either arising based on credit agreement and or Any change / renewal / renewal thereof then the debtor hereby deposits to the bank the guarantees which the transfer of ownership rights is evidenced by documents and agreements made in the form, amount and content satisfying the bank in the form of four plots of land. Constraints in the implementation of the principle of droit de preference on the guarantee of mortgage rights by the banks in the credit agreement include the difficulty of banks to conduct surveys on collateral pledged by debtors, difficulties in assessing the price of land and buildings by the bank other than that the debtor's academic ability in Understand all the contents of the agreement so that only give confidence to the bank to complete all the documentation related to the credit agreement process. The solution to overcome these obstacles is that the Bank checks to the Land Affairs Office because that is where the completeness of land documents, and the bank conducts field surveys after the credit agreement is implemented and by looking at the Land and Building Tax Payment Letters as the Value of Tax Objects and provide detailed explanation and Detailed on each article of the credit agreement so that the debtor will understand and understand each article of the credit agreement to be signed.Keywords: Droit de Preference Principle, Guarantee of Deposit Rights

DOCUMENT IMAGE REGISTRATION FOR IMPOSED LAYER EXTRACTION

Extraction of filled-in information from document images in the presence of template poses challenges due to geometrical distortion. Filled-in document image consists of null background, general information foreground and vital information imposed layer. Template document image consists of null background and general information foreground layer. In this paper a novel document image registration technique has been proposed to extract imposed layer from input document image. A convex polygon is constructed around the content of the input and the template image using convex hull. The vertices of the convex polygons of input and template are paired based on minimum Euclidean distance. Each vertex of the input convex polygon is subjected to transformation for the permutable combinations of rotation and scaling. Translation is handled by tight crop. For every transformation of the input vertices, Minimum Hausdorff distance (MHD) is computed. Minimum Hausdorff distance identifies the rotation and scaling values by which the input image should be transformed to align it to the template. Since transformation is an estimation process, the components in the input image do not overlay exactly on the components in the template, therefore connected component technique is applied to extract contour boxes at word level to identify partially overlapping components. Geometrical features such as density, area and degree of overlapping are extracted and compared between partially overlapping components to identify and eliminate components common to input image and template image. The residue constitutes imposed layer. Experimental results indicate the efficacy of the proposed model with computational complexity. Experiment has been conducted on variety of filled-in forms, applications and bank cheques. Data sets have been generated as test sets for comparative analysis.

Pixel distribution-based features for offline Arabic handwritten word recognition

Handwritten word recognition is the ability of a computer to receive and interpret intelligible handwritten input. An important document recognition application is bank cheque processing. The Arabic bank cheque processing system has not been studied as much as Latin and Chinese systems. The domain of handwriting in the Arabic script presents unique technical challenges; proposing a model for feature extraction which combines multiple types of features most likely will help to improve the recognition rate. This work proposed a pixel distribution-based features model PDM for offline Arabic handwritten word recognition. Two combination levels were used: the first combines different features and the second combination was done by ensemble classifiers. The AHDB dataset was used, and the experimental results showed superior performance when combining multiple features and using multi classifiers.

Pixel distribution-based features for offline Arabic handwritten word recognition

Handwritten word recognition is the ability of a computer to receive and interpret intelligible handwritten input. An important document recognition application is bank cheque processing. The Arabic bank cheque processing system has not been studied as much as Latin and Chinese systems. The domain of handwriting in the Arabic script presents unique technical challenges; proposing a model for feature extraction which combines multiple types of features most likely will help to improve the recognition rate. This work proposed a pixel distribution-based features model (PDM) for offline Arabic handwritten word recognition. Two combination levels were used: the first combines different features and the second combination was done by ensemble classifiers. The AHDB dataset was used, and the experimental results showed superior performance when combining multiple features and using multi classifiers.

Existing techniques in Arabic Characters Recognition (ACR)

Text or handwritten document writing recognition or understanding through characters whether online or off-line play an important role in many applications, e.g. recognizing text on bank cheques, editing old documents or translating online writing. The Arabic language characters recognition in Arabic text (hand written and printed text) is a challenging job, which is addressed by researchers in two different domains, i.e. online and off-line. The challenges are due to the cursive nature of the Arabic language. The approaches designed and adopted to achieve the target of Arabic characters recognition are also broadly classified into two, i.e. with segmentation and without segmentation. Machines can easily classify the characters if they are properly presented to it. The aim of this paper is to review critically state-of-the-art and to highlight the drawbacks in recent Arabic character recognition (ACR) for realization of the needs for robust online or off-line character recognition. This paper will properly enlighten the way for new researchers to improve or overcome the problems faced by previous studies and in addition provide a brief summary of ACR databases.

The Quality of Institutions and Organizational Form Decisions: Evidence from Within the Firm

In many retail and service sectors, firms have to establish a physical presence in a geographic market to access customers there. In countries where the quality of institutions is low, this can put assets at risk. We use data on the operations of a multinational, multibrand hotel company to show that in environments where local institutions are weaker—as proxied mainly by the World Bank's Checks index—the company eschews direct ownership. Rather than increasing its reliance on franchising, as predicted by some models, the company relies more on another form of organization commonly used in this industry, namely management contracts. We explain these patterns by emphasizing how the quality of the institutional environment affects the cost of using equity‐based organizational forms, per arguments in the current literature, but also the cost of enforcing the terms of franchise contracts.

A Novel Bank Check Signature Verification Model using Concentric Circle Masking Features and its Performance Analysis over Various Neural Network Training Functions

Background: Handwritten signature is a person's unique identity. Signature verification is an economical biometric method with online and offline schemes. This paper deals with the offline verification of signatures found in bank checks. Method: Extracting feature is the most vital part of a signature verification process. An efficient feature extraction method, Concentric Circles Masking Method, is used to extract robust, scale invariant and rotation invariant features. The extracted feature values are normalized and fed to a feedforward back propagation neural network for classification of the signatures into genuine or forged ones. The feature's performance is measured with various training functions of the neural network. The system modeled is tested with the well-known CEDAR database. Findings: Experimental Analysis shows that the features extracted by this method prove to be efficient. The scanned signature is covered by concentric circles and the pixel distribution ratio in each circle is calculated and used for verification purpose. Since a circle is used, the extracted features are scale and rotation invariant which makes the feature robust. The neural network's training, validation and testing ratio are varied and the performance of various training functions is studied. It is inferred that conjugate gradient back propagation with Fletcher-Reeves updates (traincgf) training function has the maximum average accuracy of 97.89% for the CCMM features.

Study and Analysis of Different Anti-Counterfeiting Features in Security Printing Jobs

Security printing faces maximum challenges from counterfeiters. To prevent them, the security documents contain special anti-counterfeiting features or security features which are difficult to forge. As counterfeiters are using latest technology, it is essential to develop more improved and complex anti-counterfeiting features which are impossible to reproduce. For this, it is important to study and analyze different existing anti-counterfeiting features in various types of security documents. For this purpose, some samples of such documents (banknotes of Rs. 1000, Rs. 500, Rs. 100, Rs. 50, Rs. 20 and Rs. 10, bank cheques, Aadhaar card, voter id card, pan card) have been collected and different types of methods have been used to identify a counterfeit security document.

Recognition of handwritten digits using the signature features and Optimum-Path Forest Classifier

There is a growing need for recognition of digits manuscripts for use in various situations, such as recognition of handwritten postal address digits for automated redirection of letters in the mail, acknowledgment of nominal values in bank checks. Recognition of handwritten digits faces great difficulty in dealing with intra-class variation due to different writing styles, different degrees of inclination of the characters. Optical character recognition systems, also known as OCR, identifying and recognizing printed characters through images, an already widespread functionality in scanners, mobile devices, among others. This paper presents the use of the classifier Optimum-Path Forest (OPF) applied in handwriting recognition digits. A new feature extraction method is proposed using signature of the characters, and the OPF algorithm is used in the classification. According to the results presented, it appears that the detection and recognition of characters are being carried out satisfactorily in the Manhattan distance stood out with an average accuracy of 99.53%, and get training times and test lower than the other methods such as It is the characteristic of OPF method.

Enhanced structural perceptual feature extraction model for Arabic literal amount recognition

One of the important applications for document recognition is the bank cheque processing, which is known as cheque literal amount. A few studies focused on Arabic bank cheque processing system compared to other systems, such as Latin and Chinese. The Arabic script has a number of characteristics that makes it unique among other scripts. It is known that humans are the best pattern recognisers. As such, the features detected while human reads the script can get better recognition rates. Therefore, proposing human reading inspired features which are called perceptual features can overcome the unique technical challenges in Arabic literal amount recognition. In this paper, the enhanced structural perceptual feature extraction model PFM has been proposed. Two main groups of features, which are the components and dots features and the loops and characters shapes features were combined to construct the PFM. This model was evaluated on standard Arabic Handwriting DataBase AHDB dataset. The PFM results outperformed the results reported in the previous studies.

Handwritten Malayalam Character Recognition using Curvelet Transform and ANN

Malayalam, the official language of Kerala, a southern state of India has been accorded the honour of language of eminence. Hence the researches in recognition and related works in Malayalam language is gaining more prominence in the current scenario. This paper proposes the use of Curvelet transform and neural network for the recognition of handwritten Malayalam character. Curvelet transform is to be used in the feature extraction stage and neural network for classification. Curvelet transform provides a compact representation for curved singularities and is well suited for malayalam language. Two different back propagation algorithms had been employed and the performance is compared on varying architecture. The promising feature of the work is successful classification of 53 characters which is an improvement over the existing works. Application of character recognition include sorting of bank cheques and postal letters, reading aid for blind, data compression etc. Besides, an automated tool with graphical user interface in MATLAB has been developed for Malayalam character recognition. General Terms Pattern Recognition, Artificial Neural Network (ANN), Curvelet Transform, Optical character recognition (OCR),

Handwritten Courtesy Amount and Signature Recognition on Bank Cheque using Neural Network

Bank cheques are used not only in our country but also all over the world for financial transactions. Still now Bank cheques are processed manually everyday in both developed and developing countries. The proposed system presents a recognition system of both handwritten courtesy amount and signature .To read the courtesy amount and signature, the system uses the scanned image of Bank cheque. The proposed system divided into several stages that focus on: Image preprocessing; the detection of block of courtesy amount and signature; the post processing; the segmentation of string into characters; Feature extraction of courtesy amount and signature; Neural Network recognition. At first, scanned image is converted into gray image and then it is filtered. Then detection of the courtesy amount and signature is performed using image cropping method. The cropped images are post-processed to ensure correct recognition. Then courtesy amount is segmented using the segmentation method. The segmentation module has been implemented as a recursive process. Then segmented digit and binary image of signature are passed through the feature extraction process. A rotation, translation, scaling and orientation invariant feature extraction method has been used to extract the features of the input images based on moment feature extraction method. And finally, a back propagation learning algorithm is used to train up the network and tested the performance. Overall success rate of the system is tested in different sorts of numeral and the experimental result shows satisfactory performance.

Design of Bank Cheque Validation System

Design of Bank Cheque Validation System

UV Signal Detection and Analysis of China Bank Check

This article describes the classification of China bank check, green fluorescent reaction under ultraviolet light source illumination of the featured regions—— invisible flower mission and motif pattern, and recognize the accuracy of detecting bank check feature area by signal processing and pattern recognition of UV green fluorescent response signal receiver at particular locations. It described the light filter for bank check, which is different from RMB Banknote

IJARCCE - Computer and Communication Engineering

Handwritten digit recognition is one of the important problems in computer vision these days. There is a great interest in this field because of many potential applications, most importantly where large number of documents must be dealed such as post mail sorting, bank cheque analysis, handwritten form processing etc. So a system should be designed in such a way that it is capable of reading handwritten digits and provide appropriate results. This paper presents a survey on various neural network approaches to recognize handwritten digits.

A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications.

Using Forgery Documents in Bank Operations of Iranian Criminal System

This Research survey Using Forgery Documents in Bank Operations of Iranian Criminal System. The result of this paper showed that bank activity is economic, the nature of the crimes committed in connection with a bank typically has an economic nature and financial incentives play a fundamental role to commit these crimes. Banks today as one of the most important economic lever of society in general and the government in particular is considered and despite the efforts that have been made in recent years to nonstate economy, also the bank in the Iran community, like all other communities, retains its state form and it seems that the government uses the bank as a lever to control the economy. 1. Forgery and use of forgery documents in bank operations In bank operations (cash operations - transfer operations), it is likely that bank staffs and clients with numerous motivation to commit criminal acts forgery for used documents of banks and used forgery documents in the banking operations this causes the bank system is disrupted for example, one instance of forgery offense for which the punishment is severe, is banknotes forgery. Everyone used domestic or foreign currency notes or bank documents, such as bills accepted by banks or bank checks and documents or securities or money orders issued from treasury intended to disrupt bank or momentary or economic status or disrupt the security and political and social systems, forgery or enter the country or with the knowledge of being false to use so they are not corruptor and combatant they will be sentenced to imprisonment from 5 to 20 years. 1.1 Forgery bank documents Forgery bank documents is one of major crimes violating public trust and disruption in the bank and economy system of the country and one of the most important factors of social inefficiencies in the society. Due to development of national and international network of contacts and exchanges, and need to quickly and easily perform contracts and transactions, and protect their credit and their vital role in all aspects of human life, is competent authorities concerned with the formulation of specific rules and regulations provide confidence and security of individuals, companies and organizations in bank activities. This section of paper seeks to examine the concept and types of bank documents and examples of forgery bank documents on court procedures.

Designing a new standard structure for improving automatic processing of Persian handwritten bank cheques

Millions of handwritten bank cheques are processed manually every day in banks and other financial institutions all over the world. Substitution of manual cheque processing with automatic cheque reader system saves time and the cost of processing. In the recent years, systems such as A2iA have been made in order to automate processing of Latin cheques. Normally, these systems are based on the standard structures of cheques such as Check 21 in the USA or Check 006 in Canada. There are major problems in traditional (currently used) Persian bank cheques, which yield low accuracy and computational cost in their automatic processing. In this paper, in order to solve these problems, a novel structure for Persian handwritten bank cheques is presented. Importance and supremacy of this new structure for Persian handwritten bank cheques is shown by conducting several experiments on our created database of cheques based on the new structure. The created database includes 500 handwritten bank cheques based on the presented structure. Experimental results verify the usefulness and importance of the new structure in automatic processing of Persian handwritten bank cheques which provides a standard guideline for automatic processing of Persian handwritten bank cheques comparable to Check 21 or Check 006.