Image Data Mining of Check Transactions In Support of Customer Relationship Management at Banks

Transcription

1 Image Data Mining of Check Transactions In Support of Customer Relationship Management at Banks Khaled Hassanein, Michael G. Degroote School of Business, McMaster University, 1280 Main Street West Hamilton, Ont., L8S 4M4 Canada Abstract We are currently witnessing a rapid transition from a paper-based economy to a digital-based one. As this transition progresses, it will become increasingly easier for businesses to capture customer-related information and use it to support a more sophisticated approach to direct marketing and customer relationship management. However, this transition is not yet complete, as many of the customer transactions are still paper-based. In such an environment, relying on only the digital stream of transactions is bound to result in a skewed understanding of customers. Businesses who seek to gain a true understanding of their customers, must therefore, be able to leverage information through all the channels in which they come into contact with their customers, including paper-based ones. This paper will focus on exploring the importance of analyzing the information content of check transactions as a means for understanding banking customers. Check transactions represent a major information-rich stream of customer data that is available to banks today. However, this valuable source of data is largely untapped due to the difficulties associated with extracting data from check images. The paper will show how it is possible to exploit image processing and pattern recognition techniques to help in extracting and analyzing the information content of check transactions. It will also explore how this information could be used within the context of a data warehouse to provide banks with a better understanding of their customers, allowing them to approach those customers with customized offers for products or financial services. The paper will also touch on the implications of this business application to consumer data privacy and how technology could be used to support the privacy needs of individual consumers while still supporting the business objectives of financial institutions. Keywords Check transactions, Banks, Customer relationship management, Data mining, Data warehousing, Image processing, Pattern recognition, Consumer data privacy. Phone. (905) ext hassank@mcmaster.ca 1

2 Introduction U.S. banks have been under an increasing level of pressure in recent years due to a higher level of competition as a result of deregulation. During the 1990s, banks managed to maintain decent levels of profits through mergers and acquisitions that allowed them to cut costs. However, banks are starting to realize that the traditional policies of cutting costs and increasing fees are no longer adequate to ensure their continued profitability. Instead a focus on maximizing revenue generation from individual customers and specific customer segments is emerging as the trend of the future. To achieve this goal, banks need to get closer to their customers to understand their spending behavior, their wants and needs, and their lifestyles. This understanding will give banks the ability to determine who their best customers are, which products and services they would be interested in and what needs to be done to retain them. However, banks are finding it increasingly challenging to get closer to their customers to achieve this level of understanding. Reasons of this situation include the initiatives at most banks to reduce costs by cutting down the number of branches and encouraging customers to use alternate channels to conduct their business with the bank. These channels include Automated Teller Machines (ATMs) as well as telephone banking and, more recently, Internet/Online banking. Such initiatives were successful at cutting costs and providing convenience for customers in conducting their banking anytime and anywhere. However, a negative impact of this trend has been that banks are less frequently in direct personal contact with their customers, which prevents them from gaining a high level of understanding of these customers. At the same time these customers are becoming more demanding and less loyal due to the increasing number of alternatives they have for conducting their business at other competing financial institutions. 2

3 As a result, banks are starting to rely more heavily on relationship technologies, such as data warehousing and data mining, to help them learn more about their customers, by analyzing the millions of daily transactions through which they come into indirect contact with these customers (Gartner Group Inc., 1998a). These technologies can support this goal by automatically sifting through large transaction volumes to uncover hidden trends that could help in selling new services to particular customers or help in retaining profitable customers, who might have otherwise opted to switch banks. Analyzing electronic financial transactions such as debit or telephone payments is straight forward as the data is readily available in an electronic format. As such, electronic transactions were the first channels to be subjected to data warehousing and data mining at banks. However, banks continue to ignore the single largest source of customer transactions available to them today, which is checks. This is mainly due to the difficulties associated with extracting information from this paper-based form of payment. In the following sections the check transaction is examined along with a discussion on the challenges involved in extracting information from its various fields. The technology of image data mining for extracting data from check transactions is then introduced. A methodology for integrating check image data mining and the data warehousing and mining operations at the bank is then presented. Issues related to consumer data privacy are then examined. Finally, some conclusions and future directions in this area are outlined. The Check Transaction Paper check transactions represent a significant proportion of all cashless transactions worldwide (Committee on Payment & Settlement, 1995). This proportion is especially high in the U.S. as Americans continue to exhibit a high affinity to checks as their preferred method for making non-cash payments. According to a report published by Green Sheet Inc. on check usage in the U.S. (Green Sheet Inc., 1999): 3

4 89.1% of Americans have a checking account In 2000, Americans are estimated to have written over 70 billion checks worth over 58.7 trillion dollars Checks represent 65-70% of all cashless transactions in the U.S. Although checks are starting to represent a shrinking proportion of both the total number and total value of all cashless transactions, they are predicted to continue to grow in absolute numbers at an annual rate of 2-3% through 2005 (Gartner Group Inc., 1998b, Green Sheet Inc., 1999, & Engen, 2000). This situation is due in part to the failure of U.S. banks to introduce significant incentives for their customers to start shifting to electronic forms of payments such as the debit card. This is not surprising given the fact that check revenues represent a significant proportion of the profits of U.S. banks in the form of fees and float (Engen, 2000). In addition to being such a popular payment mechanism, a check transaction contains a wealth of information. Figure 1 outlines the various fields of information on a typical personal check. Take in Figure 1 here These fields and the information they convey about the bank customer could be summarized as follows: Payor field: Who is making payments to my customer? Payee field: Whom is my customer paying (business, or individual)? Date filed: When and how frequent are certain payments made by the customer? Memo field: What is the reason for the payment? Courtesy amount field: The payment amount in numerals. Legal amount field: The payment amount in words. Payee Signature: A security feature. 4

5 The Code Line: The branch and account number of the payor at his/her home bank. Check Image Data Mining Banks are faced on a daily basis with a huge number of checks that must be processed and reconciled as soon as possible in conformity with state and/or federal regulations. This situation has motivated many banks to invest in automated check processing systems based on imaging in order to make their check processing operations more efficient. Check Amount Recognition A major application of image processing at banks is the automated reading of financial document amounts (Hassanein et al., 1996, Houle et al., 1996, Lam et al., 1995, and Moreau, 1992). This application relies mainly on the technologies of image capture, image processing, document analysis and pattern recognition. The primary motivating factors driving this technology is the demand for reduced labour costs and increased processing speeds for check processing operations. An overview of the steps involved in recognizing the amount on a typical personal check is provided in Figure 2 and can be summarized as follows: Take in Figure 2 here Obtain a grayscale image of the paper document. Convert the grayscale image to a binary (black and white) image through a binarization algorithm that suppresses the background noise (e.g. graphics) and keeps the important image pixels representing actual check transaction information. Several binarization algorithms are available in the literature (Bernsen, 1986, Hassanein et al., 1997, O Gorman, 1994, Otsu, 1979, Pavlidis, 1993, and Trier et al., 1995). Identify the exact location of the courtesy amount zone by searching for the dollar sign or the courtesy amount box. Identify the connected components (e.g. numerals, decimal point, fraction line, etc.) within the courtesy amount zone. 5

6 Use heuristic rules to assess whether the courtesy amount at hand is written in a decimal or a fractional format. Pass the identified connected components through a segmentation algorithm that separates any connected or touching numerals into individual characters. Pass the images of the individual characters through a character recognition classifier trained to recognize numerals with high accuracy. Different classifiers are used to handle hand-written and machine-printed amounts. Ultimately, a classifier returns a recognized amount and an associated confidence. This confidence is crucial for check reading applications since high accuracy is extremely important. Hence, if the system returns a confidence exceeding a specified threshold the amount is accepted as read correctly, otherwise, the document is rejected and is routed out for manual processing. The threshold is set in such a way so as to maintain the number of misread amounts below a desired level. One major obstacle facing this technology is the very high accuracy rates required by banks for these systems. Due to the high costs associated with detecting system errors, an error rate not exceeding 1% to 3% is typically desired. The high volume of checks that has to be processed each day, also means that automated financial document processing systems must be both robust and efficient. The high accuracy rates required for this application are difficult to achieve, due to the high level of variability that exists in the documents that are processed through these systems. This variability exists on many levels that are outlined in (Hassanein et al., 1996) and are summarized here: Inter-document variability: Many types of financial documents must be processed (e.g. personal checks, business checks, deposit slips, cash tickets, and traveler checks). These document types vary in their content, layout, and background. 6

7 Intra-document variability: Even within the same document type, variations exist depending on the issuing institution. Although some ANSI standards exist for personal and business checks, they are generally ignored on business checks resulting in a very high level of variability for this document type. ANSI standards are followed more closely for personal checks in terms of general layout; however, a high level of variability exists in personal check backgrounds even for checks issued by the same financial institution. Writing style variability: This is one of the major causes of accuracy problems for this technology. In hand written documents, wide variations exist in the writing style across the population. Some of these variations are geographically dependent, while others depend on the level of education and personality of the writer. Machine printed documents on the other hand, have variations that depend on the printer or typewriter and the character font used. Another source of variability is the amount writing style (e.g. fractional versus decimal Courtesy Amounts (CA) or Legal Amounts (LA) with a numeric cents field versus those with no numeric cents field). Several financial document-processing systems have been developed and are in active use at many financial institutions today. These systems typically rely on combining decisions from multiple amount recognition engines in order to achieve the required levels of accuracy. They also often concurrently use both courtesy and legal amount recognition engines to achieve higher accuracy rates (Hassanein et al., 1996 and Houle et al., 1996). Vendors providing products in this area include: NCR ( Parascript ( and Unisys ( Payee/Payor Recognition 7

8 Recognizing the check amount is critical for processing the check. The check amount could also be an important piece of information in attempting to understand the spending behavior of the customer. However, it is of little value on its own, as it does not indicate the nature of the expenditure or the deposit made by the customer. This level of understanding can be achieved by analyzing the content of the payee and payor fields of the check. Typically, a bank processing a check is in position of either the payee or the payor information associated with that check depending on which side of the transaction the bank is processing. Three scenarios could be identified in this regard: At the Bank Of First Deposit (BOFD) the payee depositing a check is known to the bank through the deposit transaction. So the missing information is the payor. At the home bank, which is processing checks written by its customers and received back from the various BOFDs, the payor or check writer is known through the check s code line. So the missing information is the payee. In check transactions involving a payee and a payor banking at the same institution, the bank is in position of both pieces of information. Recognizing the missing payee or payor information in the first two scenarios could definitely improve a bank s understanding of its customers and consequently help it in relating to those customers in a better way. Some potential applications include: The ability to identify profitable customer segments and target them for marketing campaigns that are tailored to their interests. The ability to analyze the spending habits of customers and generate statistics to help identify profitable new services to offer those customers. The ability to analyze the activities of competing firms with the bank s customers. 8

9 As such, extracting and understanding the payee/payor information from check images represents a potential revenue generating opportunity for banks, if implemented within the context of an overall Customer Relationship Management (CRM) program. However, the automated extraction of payee/payor information from check images is a relatively complex task compared to amount recognition. This is the case since, in addition to the difficulties cited above for the amount recognition task, being an unconstrained pattern recognition problem further complicates payee/payor recognition. The additional problems associated with this task are summarized below: Business checks generally do not adhere to a standard with respect to the various locations of particular fields on the check. This makes the extraction of such information as the payee and the payor very difficult. Although personal checks do adhere to a standard with respect to field locations, they pose a challenge with respect to the variability that exists in the handwriting styles of different people. Although the location of the payee field on personal checks is known, the exact location of payee text within that field could vary widely. In both business and personal checks there is a lot of interference from graphics in the background and from text intrusions from other fields close to the payee/payor fields. The name of the payee/payor could be anything written in English characters. So unlike natural language understanding applications, no syntax rules exist to limit the possibilities and unlike courtesy or legal amount recognition no rules exist to parse the payee/payor text. A Methodology for Payee/Payor Recognition As outlined above, automatically recognizing all payees/payors on personal or business checks is a rather challenging task. Further, the enormous number of checks processed by 9

10 banks everyday and the restrictions on the time taken to process these checks once they have been deposited, make it economically unfeasible for most banks to go through the tedious process of extracting this information manually. The challenge is then to find a way that will enable banks to extract this type of information automatically while maintaining a relatively high level of accuracy. The first step in arriving at a solution for the payee/payor recognition problem was to carefully analyze the true business needs of banks in using this information. Along those lines the following observations were made: Banks have little interest in recognizing all payees or payors on checks. Rather, they are most interested in identifying this information for transaction that took place between their customers and known competitors of the bank in such areas as investments or insurance. Banks can utilize such information to understand the interest of their customers in spending in certain areas and consequently be able to offer them similar services at the bank. This is a critical point as it serves to limit the variability encountered in the payee/payor fields of business and personal checks, thus making it a relatively much easier recognition task. Two distinct business cases were identified for extracting payee/payor information from checks. First, at the BOFD where the payee is known and the payor is unknown, the bank will mostly be interested in checks received by its customers from businesses competing with the bank for the customers wallet share. Hence, the main interest will be to recognize business payors on business checks. Second, at the home bank where the payor is known but the payee is unknown, the bank will mostly be interested in checks written by its personal customers to competing businesses. Hence, the main interest, in this case, will be the recognition of business payees on personal checks. 10

11 Unlike applications involving check amount recognition, applications involving payee/payor recognition can be more tolerant to recognition errors. This is the case since the most damage that could result from an error in recognizing the payee at the home bank or the payor at the BOFD will involve targeting an extra customer for a particular marketing campaign or overlooking a promising candidate for a particular offer. Banks already commit these types of errors in their everyday marketing operations. Thus, having access to some payee/payor data with a reasonable level of accuracy could only improve this situation. Having identified the business needs of the payee/payor recognition system, the following solution components were proposed and implemented: Payee/Payor reference lists: The business needs identified above could be realized through the use of a reference list of business payees/payors of interest at home/bofd banks respectively. This reference list can be used as a limited dictionary for the recognition engines used for recognizing the payee/payor information. Such a list would contain all the payees or payors of interest to the bank and is critical in limiting the variability encountered in these fields consequently enabling an acceptable rate of accuracy in recognizing these fields. This list is maintained by the bank and can be changed at any time to add new payees/payors of interest or to remove others who are no longer of interest to the bank. This idea is key to arriving at a successful solution to this problem as it essentially transforms this recognition task from one which has an unlimited search domain (recognize any payee/payor) to one with a limited search domain (verify whether specific payees/payors appear on a given image of a check). Payee text location and recognition in personal checks: Although the location of the payee field on personal checks is known, the exact location of the payee text within that 11

12 field varies from check to check even for the same customer. To overcome this problem, a payee text segmentation module was implemented (Hassanein et al., 1998) that utilizes image-processing techniques to locate and extract the payee text within the payee field. This module was designed to clean the noise and overlapping strokes from other fields in order to increase the probability of a correct recognition of the payee. Once the payee text location has been identified, this text is separated from the rest of the image and presented to a handwriting recognition engine that is constrained by the payee reference list (Hassanein et al., 2000). The payee recognition engine attempts to recognize the payee field comparing it to the various possible payees from the payee reference list. It returns the closest candidate from the reference list (that contains an identical/similar sequence of characters). Alternatively, it could return a reject response, if the recognized payee string bears no close resemblance to any of the payees on the reference list. An alias list can also be used to normalize the different ways of writing a given payee name. As an example, the alias list will map the payees ABC Corporation and ABC Corp. to the same payee. Figure 3 illustrates the major steps associated with recognizing the handwritten payee on a personal check. Take in Figure 3 here Payor text location and recognition in business checks: As the location of the payor field is generally unknown in the case of business checks, an innovative idea was used in conjunction with a payor reference list to resolve this issue. Since most business checks are machine printed, an Optical Character Recognition (OCR) engine was used to convert all the machine-printed information on a given check into ASCII characters. The recognized ASCII character strings were then searched for sequences of characters that match payors from the payors reference list, if any (Hassanein et al., 2000). This approach 12

13 allowed the system to overcome the problem of not knowing a priori the exact location of the payor field. The performance of the payee/payor recognition system is impacted by several factors including the quality of the check images as well as the number of entries on the reference list. It is also impacted by the degree of resemblance between different entries on the reference list. The recognition typically engines used in these applications return a guess for the payee or the payor and an associated confidence. A user can set a threshold to accept or reject a guess based on its associated confidence. This will in turn control the amount of errors in recognized payee/payor information. The threshold is set to achieve a certain performance level in applications that will end up utilizing this information. Typical applications involving payee/payor data are mainly related to marketing. As such, the true performance of the system should be assessed using measures related to the intended objectives such as improvement on return on a direct marketing campaign (Ling et al., 1998). Integrating Check Image Data Mining with the Data Warehouse The check image data mining system described in the previous section is envisioned to work against check images stored in an image archive or in a multimedia data warehouse. A functional system would include the following modules: A Graphical User Interface (GUI) Module: The GUI is used to control information extraction from checks according to the needs of various users. This GUI will allow the user to specify data fields to be extracted from check images as well as to create and edit appropriate reference lists of payees/payors of interest. An Image Extraction Module: Containing the image processing and pattern recognition algorithms that are used in extracting the data from checks. 13

14 An interface to the scalable data warehouse or data mart: This is important so that CRM decisions are not based on check data only, but rather on the entire set of transactions available to the bank from the various channels (e.g. ATMs and telephone banking). Naturally, the scalable data warehouse or data mart is connected back to the various points where a customer gets in touch with the bank. This enables the bank to leverage the analysis of the information stored in the data warehouse about different customers/customergroups and target them with appropriate CRM campaigns based on their data. Some examples of possible CRM campaigns based on check data are: Identifying customers who are conducting investments outside the bank and targeting them for similar services available at the bank. Those would be customers who wrote checks to or deposited checks from competing investment firms. A bank might be able to analyze personal checks deposited by their corporate clients to learn useful facts about the customers of those corporate clients. For instance, a bank could analyze the payor field on personal checks deposited by a grocery store. This analysis might reveal that 50% of the customers of the grocery store live in a certain neighbourhood. The store could use this information to tailor future marketing campaigns. Alternatively, this analysis might discover that 30% of the grocery store customers are also customers of the bank. A joint marketing campaign could then be initiated to give these customers a 5% discount on their grocery shopping at that grocery store. This initiative could increase the loyalty of these customers to the bank. Consumer Data Privacy There are some definite privacy implications to using the information content of checks. This is the case since banks are typically only owners in transient of the check document. The true owner of this document is the customer. 14

15 The customer authorizes the bank to process the check on her/his behalf. This normally entitles the bank to use information that is necessary to fulfill its obligation to process that check (i.e. code line, date, amount and signature). Extracting and using additional information such as the payee at the home bank or the payor at the BOFD poses some important privacy issues. The severity of these issues varies from country to country depending on governmental regulations. For example, The European Union recently issued a data privacy directive to protect consumer data so that it is not processed in any way that they did not explicitly agree to (Swire et al., 1998). In the U.S., regulations are more relaxed, although both banks and customers are concerned about it. U.S. banks have moved recently to implement some self-imposed regulations on consumer data privacy to try to avoid being regulated by Congress. The Financial Services Technology Consortium ( is one such attempt. Some points that could help banks in this area include: Banks must have a clearly stated consumer data privacy policy. The data warehouse where the extracted check information is to ultimately reside, should allow the bank to block certain fields in the transaction record based on customer requests for privacy of certain information. It should also support the notion of multiple views, which allows different banking departments to obtain different views of the customer s data. Some views can be more restricted in terms of the data revealed than others. Applications that focus on analyzing groups of customers to obtain some collective statistics (e.g. 35% of customers are interested in insurance policies of some sort) are generally acceptable in most countries, as they do not identify specific customers. 15

16 Banks should have policies that would allow customers to opt-in or opt-out of being marketed to based on their transaction data. They should also allow customers to access and examine their private data. Banks should also take the measures of securing their customers data. Finally, a bank might decide to ask customers to explicitly opt into being marketed to based on their transaction data in return for lower service charges, or special bonuses. Conclusions & Discussion We are currently witnessing a rapid transition from a paper-based economy to a digital-based one. As this transition progresses, it will become increasingly easier for businesses to capture customer-related information and use it to support a more sophisticated approach to direct marketing and customer relationship management. However, this transition is not yet complete, as many of the customer transactions are still paper-based. In such an environment, relying exclusively on the digital stream of transactions is bound to result in a skewed understanding of customers. Businesses that seek to gain a true understanding of their customers, must therefore, be able to leverage information through all the channels in which they come into contact with their customers, including paper-based ones. Along the above lines, this paper has examined the various issues involved in mining information from images of paper-based check transactions at banks. Based on this work the following conclusions and observations could be made: Understanding and using the information content of a check transaction could prove to be more valuable to banks than the processing of the check itself. This is the case since extracting and analyzing this information could definitely help the bank to understand and consequently relate to their customers in a better way. 16

17 One of the important issues associated with improving the business case for imaging solutions at banks, is the ability to leverage the economies resulting from this technology through multiple applications. The business case for an imaging solution for transaction processing at banks has mainly been made, so far, based on cost reduction (e.g. manual amount-keying labour displacement). This has hindered the adoption of imaging technology by many banks. By extracting and using payee/payor information from checks, banks can realize new revenue generating opportunities due to having an imaging solution in addition to its already proven cost reduction benefits in check amount recognition applications. Banks have traditionally enjoyed a unique relationship with customers in terms of being their main vehicle for paying bills. With the proliferation of the Internet and the emergence of electronic payment schemes, this position is being challenged by third parties (e.g. financial portals). These third parties are trying to capture this relationship with the customer and relegate banks to being mere payment utilities. Banks must respond by trying to maintain that critical relationship with the customer. Imaging in conjunction with relationship technologies could support banks in moving in that direction. As banks start extracting, storing and using data from customers transactions they have to be aware of the potential consumer data privacy violations involved in such practices. In this regard, banks must have a clearly stated consumer data privacy policy. They must also ensure that their customers are aware of the different uses that their data is being put to and obtain their consent to such usage. On the other hand, vendors who are supplying the technologies used by banks to extract and use customers data, must also be aware of the privacy implications of their products and provide the technology necessary to enable banks to support their privacy policy. 17

18 Finally, it is important to note that the imaging and pattern recognition technologies described in the paper are also largely applicable to other situations involving other types of documents. These technologies play an important role in enabling businesses to include customer data from paper-based transactions in their efforts to better understand their customers. References Bernsen, J. (1986), Dynamic Thresholding of Grey-Level Images, Proceedings of the 8 th International Conference on Pattern Recognition, pp Committee on Payment and Settlement Systems, Central Banks of the Group of Ten Countries (1995), Statistics on Payment Systems in the Group of Ten Countries, ( Report No. 19. Engen, J. (2000), Checking Out E-Payments, Banking Strategies Magazine, March/April issue, ( Gartner Group Inc. (1998a), Mining for Gold: Customer Profitability Strategies in Retail Banking. Gartner Group Inc. (1998b), Paper Payments: Trends and Strategies for the New Century. Green Sheet Inc. (1999), United States Check Study, Hassanein, K. and El Badawy, O. (2000), Mining Transaction Data from Cheque Images, NCR Internal Technical Report. Hassanein, K. and Kasapinovic, S. (1998), Automated Extraction of Transactional Data from Cheque Images, NCR Internal Technical Report. Hassanein, K., Wesolkowski, S., Higgins, R., Crabtree R. and Peng, A. (1996), An Integrated System for Automated Financial Document Processing, Proceedings of the 25th AIPR workshop, SPIE. Hassanein, K. and Wesolkowski, S. (1997), Stroke Extraction from Grayscale Images of Financial Documents Based on Figures of Importance, Proceedings of the IEEE International Conference on Image Processing. Houle, G., Aragon, D., Smith, R., Shridhar, M. and Kimura, D. (1996), A Multi-Layered Corroboration-Based Check Reader, Proceedings of IAPR Workshop on Document Analysis Systems ( 18

19 Lam, L., Suen, C., Guillevic, D., Strathy, N., Cheriet, M., Lui, K. and Said, J. (1995), Automatic processing of information on checks, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 3 pp Ling, C., and Li, C. (1998), Data Mining for Direct Marketing: Problems and Solutions, Proceedings of the 4 th International Conference on Knowledge Discovery and Data Mining, pp Moreau, J. (1992), A new system for automatic reading of postal checks, in Impedovo, S. and Simon, J. C. (Eds.), From pixels to features III: frontiers in handwriting recognition, Elsevier Science Publishers, pp O Gorman, L. (1994), Binarization and Multithresholding of Document Images Using Connectivity, CGVIP: Graphical Models and Image Processing, 1994, 56 (6), pp N. Otsu (1979), A Threshold Selection Method from Grey-Level Histograms, IEEE Trans. Systems, Man, and Cybernetics, 9 (1), pp Pavlidis, T. (1993), Threshold Selection Using Second Derivatives of the Gray Scale Image, Proceedings of the 2 nd International Conference on Document Analysis and Recognition, IEEE CS Press, Los Alamitos, California, pp Trier, Ø. D. and Taxt., T. (1995), Evaluation of Binarization Methods for Document Images IEEE Trans. on PAMI, 17 (3), pp Swire, P. and Litan, R. (1998), None of Your Business: World Data Flows, Electronic Commerce and the European Privacy Directive, Brookings Institution Press, Washington, D. C. 19

20 Payee Payor Date Legal Amount Memo Line Code Line Signature Courtesy Amount Figure 1: A Typical Personal Check Courtesy Amount Zone Image Character Extraction Connected Component Analysis Amount Modeling (Decimal) Amount Segmentation Character Recognition $10.00 (Recognition result) Figure 2: An overview of a courtesy amount recognition system 20

21 Binary Image Analysis Extraction of initial binary payee box Connected components within the payee box Extracted payee text according to rules set Handwriting Recognition Engine Payee Reference List Zellers Bell Zehrs Bell Canada Ford Bell Canada & Confidence Accept/Reject Figure 3: Payee Recognition System Overview 21