Bank Cheque Processing Research Articles

Hand-Written Number Classification by Hardware Neural Network

This study explores the application of hardware neural networks for handwritten digit recognition, focusing on the implementation of the perceptron model. Handwritten digit recognition is a key technology in fields such as automatic postal sorting and bank cheque processing. In this work, a hardware neural network based on Frank Rosenblatt’s perceptron model is employed to efficiently process and classify handwritten digits. The system utilizes Verilog and VLSI technology to handle inputs in a parallel and pipelined fashion, greatly reducing computation time compared to traditional serial methods. Specifically, the designed system can recognize all ten digits in just 392 clock cycles, demonstrating significant improvements in efficiency while maintaining accuracy. Parallel processing and pipelining techniques are used to optimize performance, and experimental results show accurate identification of the test images. This research highlights the potential of hardware-based neural networks in applications such as embedded systems and IoT, emphasizing the role of hardware acceleration in advancing artificial intelligence technologies.

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.

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.

Enhancing Handwritten Alphabet Prediction with Real-time IoT Sensor Integration in Machine Learning for Image

Abstract Handwriting Recognition (HWR) is a difficult and varied discipline having applications in a wide range of fields, including banking, education, and administration. This research investigates the two main types of HWR systems: online and offline character recognition. Online HWR entails real-time input utilizing digital pens to capture dynamic handwriting traits. It’s used in contemporary gadgets like tablet computers and for signature verification. Offline HWR, on the other hand, processes scanned documents, making it important in situations such as bank cheque processing and assisting the visually handicapped. The research emphasizes the continuing potential for progress in HWR, notably using machine learning and deep learning approaches. Machine learning, a subset of Artificial Intelligence (AI), is critical in developing character recognition algorithms. The selection of an effective classification model is a vital decision, and the study uses a specific dataset to conduct a comparison analysis of alternative models to help in this process. Such assessments provide useful insights for academics and practitioners, allowing them to make more informed judgements on model development for HWR applications.

Various Models for the Conversion of Handwritten Text to Digital Text

Handwritten Text Recognition (HTR) also known as Handwriting Recognition (HWR) is the detection and interpretation of handwritten text images by the computer. Handwritten text from various sources such as notebooks, documents, forms, photographs, and other devices can be given to the computer to predict and convert into the Computerized Text/Digital Text. Humans find easier to write on a piece of paper rather than typing, but now-a-days everything is being digitalized. So, HTR/HWR has an increasing use these days. There are various techniques used in recognizing the handwriting. Some of the traditional techniques are Character extraction, Character recognition, and Feature extraction, while the modern techniques are segmenting the lines for recognition, machine learning techniques, convolution neural networks, and recurrent neural networks. There are various applications for the HTR/HWR such as the Online recognition, Offline Recognition, Signature verification, Postal address interpretation, Bank-Cheque processing, Writer recognition and these are considered to be the active areas of research. An effective HTR/HWR is therefore needed for the above stated applications. During this project our objective is to find and develop various models of the purpose.

Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network

Handwriting recognition is one of the most persuasive and interesting projects as it is required in many real-life applications such as bank-check processing, postal-code recognition, handwritten notes or question paper digitization etc. Machine learning and deep learning methods are being used by developers to make computers more intelligent. A person learns how to execute a task by learning and repeating it over and over before it memorises the steps. The neurons in his brain will then be able to easily execute the task that he has mastered. This is also very close to machine learning. It employs a variety of architectures to solve various problems. Handwritten text recognition systems are models that capture and interpret handwritten numeric and character data from sources such as paper documents and photographs. For this application, a variety of machine learning algorithms were used. However, several limitations have been found, such as a large number of iterations, high training costs, and so on. Even though the other models have given impressive accuracy, it still has some drawbacks. In an unsupervised way, the Artificial Neural Network is used to learn effective data coding. For recognising real-world data, we built a model using Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN).

Automated bank cheque verification using image processing and deep learning methods

Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. In order to perform the task of cheque verification, we developed a tool which acquires the cheque leaflet key components, essential for the task of cheque clearance using image processing and deep learning methods. These components include the bank branch code, cheque number, legal as well as courtesy amount, account number, and signature patterns. our innovation aims at benefiting the banking system by re-innovating the other competent cheque-based monetary transaction system which requires automated system intervention. For this research, we used institute of development and research in banking technology (IDRBT) cheque dataset and deep learning based convolutional neural networks (CNN) which gave us an accuracy of 99.14% for handwritten numeric character recognition. It resulted in improved accuracy and precise assessment of the handwritten components of bank cheque. For machine printed script, we used MATLAB in-built OCR method and the accuracy achieved is satisfactory (97.7%) also for verification of Signature we have used Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) as classifier, the accuracy achieved for signature verification is 98.10%.

Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network

Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu lan-guage is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recog-nition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.

High-Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject. Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition. The variation in the digits is due to the writing styles of different people which can differ significantly. Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs.

A comprehensive survey on word recognition for non-Indic and Indic scripts

The term handwriting recognition is used to describe the capability of a computer system to transform human handwriting into machine processable text. Handwriting recognition has many applications in various fields such as bank-cheque processing, postal-address interpretation, document archiving, mail sorting and form processing in administration, insurance offices. A collection of different scripts is employed in writing languages throughout the world. Many researchers have done work for handwriting recognition of various non-Indic and Indic scripts from the most recent couple of years. But, only a limited number of systems are offered for word recognition for these scripts. This paper presents an extensive systematic survey of word recognition techniques. This survey of word recognition is classified broadly based on different scripts in which a word is written. Experimental evaluation of word recognition tools/techniques is presented in this paper. Different databases have been surveyed to evaluate the performance of techniques used to recognize words, and the achieved recognition accuracies have been reported. The efforts in two directions (non-Indic and Indic scripts) are reflected in this paper. We increased awareness of the potential benefits of word recognition techniques and identify the need to develop an efficient word recognition technique. Recommendations are also provided for future research. It is also observed that the research in this area is quietly thin and still more research is to be done, particularly in the case of word recognition of printed/handwritten documents in Indic scripts.

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.

Signature Verification Using Support Vector Machine (SVM)

Automated signature verification has many applications in our daily life like Bank-cheque processing,document authentication, ATM access etc. Handwritten signatures have proved to be important inauthenticating a person's identity, who is signing the document. In this paper we present an off-linesignature verification and recognition system using the global, directional and grid features of signatures.Support vector machine (SVM) was used to verify and classify the signatures. As there are unique andimportant variations in the feature elements of each signature, so in order to match a particular signaturewith the database, the structural parameters of the signatures along with the local variations in thesignature characteristics are used. The artificial neural networks are trained by these characteristic. Thesystem uses the features extracted from the signatures such as centroid, height – width ratio, total area,first and second order derivatives, quadrant areas etc. After the verification of the signature the anglefeatures are used in fuzzy logic based system for forgery detection and the performance is increasesapproximately (80%) when using SVM as a classifier

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.

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.

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.

Arabic Bank Check Processing: State of the Art

In this paper, we present a general model for Arabic bank check processing indicating the major phases of a check processing system. We then survey the available databases for Arabic bank check processing research. The state of the art in the different phases of Arabic bank check processing is surveyed (i.e., pre-processing, check analysis and segmentation, features extraction, and legal and courtesy amounts recognition). The open issues for future research are stated and areas that need improvements are presented. To the best of our knowledge, it is the ¯rst survey of Arabic bank check processing.

Automatic processing of handwritten bank cheque images: a survey

Bank cheques (checks) are still widely used all over the world for financial transactions. Huge volumes of handwritten bank cheques are processed manually every day in developing countries. In such a manual verification, user written information including date, signature, legal and courtesy amounts present on each cheque has to be visually verified. As many countries use cheque truncation systems (CTS) nowadays, much time, effort and money can be saved if this entire process of recognition, verification and data entry is done automatically using images of cheques. An attempt is made in this paper to present the state of the art in automatic processing of handwritten cheque images. It discusses the important results reported so far in preprocessing, extraction, recognition and verification of handwritten fields on bank cheques and highlights the positive directions of research till date. The paper has a comprehensive bibliography of many references as a support for researchers working in the field of automatic bank cheque processing. The paper also contains some information about the products available in the market for automatic cheque processing. To the best of our knowledge, there is no survey in the area of automatic cheque processing, and there is a need of such a survey to know the state of the art.

Segmentation and Recognition of Handwritten Numeric Chains

Automatic reading of numeric chains has been attempted in several application areas such as bank cheque processing, postal code recognition and form processing. Such applications have been very popular in handwriting recognition research, due to the possibility to reduce considerably the manual effort involved in these tasks. In this study we propose an off line system for the recognition of the handwritten numeric chains. Firstly, study was based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. Used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: distribution sequence, Barr features and centred moments of different projections and profiles. Secondly, study was extented for the reading of the handwritten numeric chains constituted of a variable number of digits. Vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The performances of the proposed system for the used database attain a recognition rate equal to 91.3%.

HANDWRITTEN BANK CHECK RECOGNITION OF COURTESY AMOUNTS

In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates.