Automatic Segmentation and Recognition of Bank Cheque Fields

This paper describes a novel method for automatically segmenting and recognizing the various information fields present on a bank cheque. The uniqueness of our approach lies in the fact that it doesn't necessitate any prior information and requires minimum human intervention. The extraction of segmented fields is accomplished by means of a connectivity based approach. For the recognition part, we have proposed four innovative features, namely; entropy, energy, aspect ratio and average fuzzy membership values. Though no particular feature is pertinent in itself but a combination of these is used for differentiating between the fields. Finally, a fuzzy neural network is trained to identify the desired fields. The system performance is quite promising on a large dataset of real and synthetic cheque images

Recognition of off-line arabic handwritten dates and numeral strings

In this thesis, we present an automatic recognition system for CENPARMI off-line Arabic handwritten dates collected from Arabic Nationalities. This system consists of modules that segment and recognize an Arabic handwritten date image. First, in the segmentation module, the system explicitly segments a date image into a sequence of basic constituents or segments. As a part of this module, a special sub-module was developed to over-segment any constituent that is a candidate for a touching pair. The proposed touching pair segmentation submodule has been tested on three different datasets of handwritten numeral touching pairs: The CENPARMI Arabic [6], Urdu, and Dari [24] datasets. The final recognition rates of 92.22%, 90.43%, and 86.10% were achieved for Arabic, Urdu and Dari, respectively. Afterwards, the segments are preprocessed and sent to the classification module. In this stage, feature vectors are extracted and then recognized by an isolated numeral classifier. This recognition system has been tested in five different isolated numeral databases: The CENPARMI Arabic [6], Urdu, Dari [24], Farsi, and Pashto databases with overall recognition rates of 97.29% 97.75%, 97.75%, 97.95% and 98.36%, respectively. Finally, a date post processing module is developed to improve the recognition results. This post processing module is used in two different stages. First, in the date stage, to verify that the segmentation/recognition output represents a valid date image and it chooses the best date format to be assigned to this image. Second, in the sub-field stage, to evaluate the values for the date three parts: day, month and year. Experiments on two different databases of Arabic handwritten dates: CENPARMI Arabic database [6] and the CENPARMI Arabic Bank Cheques database [7], show encouraging results with overall recognition rates of 85.05% and 66.49, respectively

Feedback Based Architecture for Reading Check Courtesy Amounts

In recent years, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either on intra-organization basis or on inter-organization basis, including paper intensive applications in the check processing application. In many countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. Such process of manual reading of each check involves significant time and cost. In this research, a new approach is introduced to read the numerical amount field on the check; also known as the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: segmentation, normalization, and the recognition of each character using a neural network classifier, with results better than many other methods in the literaratu

Marketing of bank services to the Saudi consumer

Services marketing is a relatively new concept for the banking industry. Intense competition has forced banks to take greater interest in consumer banking. The focus of consumer banking is the provision of retail bank services which meet individual customers needs. Therefore, bankers need better ways to explore and understand the consumers market and needs. Banks need to understand the attitudes which affect consumers evaluation of bank services. This thesis is concerned with assessing the attitudes and marketing practices of Saudi banks and the consumers attitude towards these banks. A conflict exists between the two parties. Interest is prohibited by Islamic law, however, commercial banks operate on the basis of interest. The views of the bankers and consumers were analysed through questionnaires administered in Jeddah in the summer of 1988. Issues raised include attitude, concept, and marketing approach, usage of and satisfaction with bank services, attitude towards banks and bank interest as well as views on Islamic banking. While it is shown bankers grasp the concept and practise of marketing, consumers show reluctance and strong negative attitudes to dealing with banks due to the religious factor. It seems Islamic banking is an attractive alternative banking system for Saudi Arabia

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

Automatic Extraction of Attributes from Printed Indian Cheque Images by Template Matching Technique

Reserve Bank of India (RBI) has introduced Cheque Truncation System (CTS) for Indian banks in order to reduce the time required for physical movement of cheques between the clearance departments. However, other processes including database entry and verification are carried out manually. The proposal here is to eliminate the manual intervention by extracting the attributes from the input cheque image and updating the database automatically which significantly would reduce the time lapse on filling up the data into the database. Automatic database updating also contributes to provide secure data retrieval through querying system for verification of attributes by concerned banks. In this paper, a novel approach to extract printed attributes from Indian Bank cheque images based on their template structures is proposed. Template structures are determined by extracting the MICR code from the input cheque image. Important attributes region is segmented, and the printed data is recognized. Extensive experiments demonstrate the efficacy of the proposed method