Automated Forms Processing

Transcription

1 IMERGE Consulting, Inc January/February Document image capture and forms automation share a lot in common. They are built on the same technologies. Both involve scanning, and they each rely on image processing capabilities to enhance or evaluate a document image. In both cases, indexing and other data must be extracted from the image. The main difference between the two applications has to do with the way each one treats imaged data. With document capture, imaged data are entered (either manually or through ICR) with reference to one or a small number of index fields. With forms automation, most or all of the imaged data on each form are intelligently recognized and extracted, often from highly complex documents where ICR accuracy is mission-critical. In other words, with an image capture application, the document image is the end product. With forms automation, the document image functions simply as a means towards an end; indeed, in some applications, the form image is thrown out after the data is successfully extracted. As a result, the way that documents are organized is handled differently for image capture than for forms automation, particularly in production applications. With document imaging, scanned images of varied and mixed document types are indexed, then organized into folders for on-demand viewing. With forms processing, highly structured documents are identified and sorted by form type, then they are batch-processed using recognition templates that automatically find the data fields by using predefined location coordinate information. In the case of so-called unstructured forms, the form templates are defined more by rules, morphological analysis, and feature extraction than they are by stored, location- specific information; but in both cases, an explicit data set is detected, classified, and exported to a database. Today, merged image capture and forms automation applications are entering the high-tech mainstream, where users demand uncomplicated, user friendly applications with a short payback. The two applications are offered together in software packages that employ a common document scanning operation, and then pass the document images through predefined indexing or recognition workflows, based upon document-specific criteria. Lower computing costs, more powerful and more sophisticated technology, and an abundant IT infrastructure, all combine to promote vendor success. Comes of Age Automated forms processing has evolved considerably since the days of DOS and 286 s. For a long time, the dream exceeded the reality because unlocking the potential savings and benefits of forms automation requires a complex, corporate-wide infrastructure that integrates interdependent EDMS technologies into a cohesive whole. However, that solution was unavailable until recently (at least without spending a small fortune). Enormous advances in scanner technology, image compression, ICR, image enhancement, database management, LAN, networks, APIs, chip technology, GUIs, systems integration, and data entry ergonomics had to occur before the total forms automation solutions of today became possible.

2 The right infrastructure and software is now available at prices that are affordable for small businesses as well as for members of the Fortune 500. It is not unusual for an automated forms processing system to cut the expenses associated with the data entry activities it replaces by 60%. Multi-million dollar, high production systems are available that boast a payback of less than a year. So, with over 20 years of experimentation, hard core development, and missionary selling by dedicated salesmen in the bank, automated forms processing is finally living up to its enormous potential. Here are the main reasons why. The benefits of automated forms processing applications have been field-tested and proven. Reference sites abound. ICR, the driving technology behind image-based forms processing, has survived its trial by fire. The major industry sectors all have numerous flagship installations. Every VAR and systems integrator has an abundance of reference sites. The IRS and the U.S. Census have blessed forms automation to the tune of over $6 billion of government contracts, and the majority of states have high volume, automated forms processing systems in place that each process millions of tax returns annually. Document imaging technology, the enabling platform for forms processing, is now widely installed. When implementation of a big new imaging system was directly linked to the adoption of a forms processing application, it often was an uphill battle. But, with the advent of enterprise-wide content management and the imaging infrastructure in place and paid for, a forms processing application is now considered an add-on feature that produces tremendous added value for a relatively incremental cost. After years of vendor resistance, forms automation technology is routinely sold by vendors of workflow applications. For years, forms processing, driven by ICR, was seen as a dire threat to workflow vendors because it eliminated the need of the end user to purchase a lot of workstations for data entry operators. With the proliferation of knowledge management, content management, and other information management disciplines that require document capture and other electronic imaging applications, those workstations are now needed for tasks other than data entry. Since ICR is now no longer perceived as a threat to their livelihood, workflow vendors are pushing forms automation systems as a value-added sale. Contemporary enterprise content management systems include forms processing technology as part of their platform. The new emphasis on enterprise-wide content management systems, with forms automation technology included in the package as a part of either the document management or workflow subsystems, means that forms processing is considered to be a mainstream application. Web-centric, e-forms applications have been successfully combined with intranets and Internet portals to enable corporate users to create form-based applications that extend their business processes through the enterprise directly to the end user. In these e-form applications, an electronic form becomes a browser-based GUI that facilitates the interaction between the user and a database or with other people in the corporation. Examples of internal applications include inventory management and supply chain management systems that allow departments within corporations to maintain up-to-the minute knowledge of and control over, all information regarding those applications. Onsite data is accessed through a corporate wide, browser-based intranet, while remote branches can access that same data over the Internet. External applications would include extranets that electronically extend the company business process directly to the end user, as with mortgage and loan applications, and mutual funds tracking and investment programs. Image capture and forms processing, once perceived as separate applications, are now offered to users as one comprehensive solution by software vendors. This technology 2

3 advance eliminated a number of software and hardware redundancies and, in the process, created three major advantages for end users: (1) easier and more effective training for operators on one system versus two; (2) better, more efficient, less costly administration by maintaining only one system; and (3) a higher return on investment due to increased efficiencies and lower costs. Advances in cursive handwriting ICR, driven by Check 21 legislation, are driving the implementation of numerous check recognition installations. A check is a form. Legal amount recognition (LAR) of the data field that contains the handwritten amount of the check, when interactively compared to the courtesy amount recognition (CAR) of the numeric amount field in a check, produces field recognition accuracy of 99% on accepted fields, while leaving only 18% of the checks behind for data entry operators to correct! Check 21 legislation, which allows check images instead of paper checks to be processed by the Federal Reserve, is expected to dramatically increase check recognition technology adoption by banks over the coming years as the Check 21 rules takes effect. The digital mailroom is an emerging reality. The digital mailroom is a high-volume application that involves putting a scanner in the mailroom to convert all incoming mail to electronic images and then applying ICR to recognize and classify all incoming documents and customer correspondence. The data is sent to a central repository along with correspondence, a procedure that allows incoming business data to be merged into one workflow and then managed by one work process. Today, the hottest document management application driving forms automation software sales is the processing of unstructured forms such as purchase orders, invoices, and explanation of benefits (EOBs) forms. Success in processing unstructured documents significantly broadens the application universe that depends on automated forms processing. Given a wider variety of forms to process, forms automation solutions are being adopted particularly at highvolume installations by users who otherwise would not be motivated to implement them. Growth will continue, provided that (1) an acceptable degree of accuracy can be consistently achieved and (2) automated processing of unstructured forms can create labor savings comparable to those already attainable by systems that process structured forms. Taken together, these factors are driving the adoption of forms automation systems to the point where, in combination with other EDMS applications, they can reliably and consistently enable the successful extraction and the management of virtually all paper-based data that enters a company. Of course, forms processing software does not completely automate data entry tasks, because human intelligence is always required to correct the exception errors committed by machines. But users now understand that the function of automated forms processing applications is not to replace human labor altogether, but rather to ease the arduous task of converting paper-based information into computer- usable data. Steps in the Template-Based Processing of Structured Forms Just as documents must be prepared in order to be fed into a scanner by removing staples, smoothing wrinkles, positioning them for optimal registration, etc., so the image of a form document must be prepared by following these steps before it can be intelligently recognized: Document scanning Pages of forms are scanned and converted into bit-mapped (usually TIFF) images of forms which are either compressed and stored for later batch processing, or are passed immediately in an uncompressed format to an ICR engine for recognition. Image analysis The document image is cleaned up. Character image quality is improved, using image enhancement techniques. Background noise is removed from the form. 3

4 Form alignment The image is registered and deskewed by the ICR software, which automatically aligns the form by locating special symbols on the document called registration marks as guides. Form identification The document is identified by certain predefined characteristics that the ICR software is trained to look for, so that the zones containing the fields designated for recognition can be located by a customized, predefined ICR template. Form ID attributes can include form numbers, corporate logos, or the name of the form itself imprinted somewhere on the form. Form background removal This stage is not necessary if the document is a form that was originally printed in a colored ( drop out ) ink that is invisible to the scanner being used. If colored ink is not used, the form image may contain lines, boxes, fine print, and other form attributes passive data that tend to confuse the ICR engine. These form attributes must be extracted from the image of the form, so that only the character images the active data are left behind. Broken and fragmented characters are automatically repaired and restored to their original shapes. Character field location The predefined ICR template automatically locates the fields that contain character data. The template identifies which individual fields on the form image require character recognition, and what the nature of those fields are hand print, machine print, numeric, alphabetic, alphanumeric, etc. The template also identifies which areas are barcodes or check box recognition zones. Character segmentation Sophisticated software routines analyze, separate, and break down the character fields into isolated characters. If the form is ICR friendly, characters are segmented with the aid of graphic devices such as boxes, tick-marks, and connected boxes called combs that serve to force the form user to legibly separate the characters from one another. Character classification Individual characters are classified by ICR algorithms according to their ASCII category and assigned a confidence value, which is an index of how certain the ICR engine feels about the selection it has made. Alternate character choices are ranked according to those values, so that they can be incorporated into editing procedures that improve ICR accuracy. For example, the alternate choice 1 might be used instead of the first-ranked choice I when contextual analysis reports that the field is all-numeric. Post-processing The initial or raw recognition results are validated using edit procedures such as grammatical rules, spell-checkers, dictionaries, check-sum routines, and look-up tables. Ambiguous and erroneous data fields the rejects are identified and sent to data entry operators at workstations for manual correction. Manual correction of rejected character fields The manner in which the data entry operator is presented the rejected data for correction can dramatically impact both the speed and the accuracy of the reject repair process. In particular, the data entry GUI is important because the ergonomics of data entry are what enable a given data entry operator to reach his or her maximum correction speed. What is interesting is that only one of the steps character classification is specifically concerned with identifying character data. The rest of the steps have to do with either preparing the imaged characters for classification or interpreting the results of character classification. With so much opportunity for error increasing at each successive step of the way, it is remarkable that ICR accuracy rates can attain (and sometimes exceed) human performance levels. Processing Unstructured and Semi-Structured Forms When it comes to recognizing unstructured or semi-structured forms and other documents types, these same steps still apply, although in a different order and manner. On unstructured forms such as invoices, EOBs, and transportation documents, locating variably-formatted data is the number one priority. The presence of random differences over a series of functionally similar forms means that the forms in question cannot be processed using the traditional template-based approach, in which 4

5 one software template matches each and every data field on each and every form in a presorted batch. Multiple approaches are required, approaches that create alternatives with results that are compliant with classifying similar data elements. Given the incredible computing horsepower and enormous amount of memory that reside in the average desktop PC, a variety of techniques, including ICR brute force, can be run in parallel to achieve remarkably accurate results that were impossible to archive only a few years ago. Sometimes, morphological analysis techniques involving blob analysis, edge detection, multi-line character segmentation, and long-line detection can be used to find form objects, columns, and data fields. At other times, the geometrical and spatial relationships between the text data elements, such as rows or subheadings (rather than graphical objects), can locate the places where data most likely will be found. In fact, the data location process need not involve character recognition at all; the text can be treated as integral patterns of blobs. Some Features To Look For in a Forms Processing System As traditional recognition problems are solved, software developers introduce new product features that address increasingly sophisticated issues in document capture and data extraction. Taken together, they can vastly improve recognition accuracy. These features include: Adaptive thresholding. The optimum threshold for binarizing hand-print characters for accurate character recognition is determined by sophisticated image analysis algorithms and applied on the fly. Intelligent address extraction. Locating and exporting addresses into a preformatted database. Page segmentation. Automatic decomposition of a form into graphics, text, logos, and other data elements. Conditional procedures. Method in which specific data fields are located on the fly and then applied to a procedure or mathematical operation. Spreadsheet reference tools. Spreadsheet tables, employing up to120 different functions, can be used to validate data. Multi-language recognition. ICR of languages in addition to English up to 173 different languages in one vendor package. EDI emulation. Data is formatted and rearranged to appear as EDI (electronic data interchange) output to an EDI-formatted database. Workflow routines. Designed for inserting data into the business processes of a company. Internet publishing. Data from scanned forms is immediately posted to Web sites. Font learning. Machine print recognition engines can be trained by the end user to recognize new fonts. Dot-matrix enhancement. Dot-matrix characters created by old impact printers are automatically filled in and reconstructed by special algorithms for classification by an ICR engine. PDF export. Export of recognition results to Adobe PDF format. PDF text extraction. Drag and drop capability to automatically read embedded text in an Adobe PDF file and retain the text formatting for export. PDF recognition. Conversion of embedded image data files to formatted text, when Adobe PDF documents contain text based, JPG images instead of the text itself. XML export. Export of form recognition results to XML format. CJK (Chinese Japanese and Korean) OCR. Recognition of machine-printed Asian languages, some of which are pictographs. Support for 2D-barcodes (PDF 417). Capability to recognize 2-D barcodes and also to export recognition results to PDF 417 barcode format. Document analysis for invoices. Capability to intelligently analyze financial data in documents and then export the results in the native format of a variety of accounting packages. Application-specific, plug-in software modules. Customized application packages for recognizing invoices, claims, tax forms, and other challenging documents. 5

6 In addition to ICR, OCR, and other recognition-oriented features, today s forms automation software vendors offer a wide range of sophisticated options and capabilities that contribute to bottom-line throughput. These include: scanner driver support; use of multiple recognition engines; fax support; integration with workflow and workload distributor modules; plus a host of output management options, including back-end integration with COLD/ERM systems, back-end integration with financial software, back-end integration with printing packages, and back-end integration with e-commerce applications. Taken together, these features enable and accelerate the application integration process and help to accelerate the rate of forms processing technology adoption within the enterprise. Characteristics Of an Ergonomically Correct User Interface One of the major incentives to implement a forms automation system does not involve labor savings, but instead entails reducing legal and medical costs. These costs are generated by complications from repetitive strain injury (RSI), complications that are especially common among the population of workers who have little formal training in typing. Emil F. Pascarelli, in Repetitive Strain Injury: a Computer User s Guide, reports that the number of RSI incidents has increased dramatically since the first IBM PC was released in 1981, when only 18% of all illnesses reported were RSIs. However, in 2000, an estimated 70% of all occupational illnesses reported to OSHA were RSIs! Such injuries cost American companies some $20 billion a year in legal and medical costs expenses that American companies are eager to minimize by replacing data entry workers with ICR technology, and providing a working environment that guards workers against RSIs. When it comes to high volume, homogeneously batched, keyfrom- image (KFI) or ICR error correction applications, then data entry ergonomics are of paramount importance. It is imperative that a data correction interface incorporate field-tested, ergonomically sound features that can maximize data entry productivity while they ease screen navigation and keying movements, thereby minimizing eye and muscle strain. Comparing the data correction GUI modules developed by a variety of vendors uncovers similar approaches to dealing with the critical factors that enable efficient, imaged-based, character correction and key entry speed. An ergonomically correct user interface should include the following features. A robust, multi-tasking operating system. This is necessary to support image caching, which enables rapid forward and backward queuing, plus capabilities such as multiple field display; automated work distribution; and pipelined, hierarchical edit and control procedures. Pre-fetching and preloading algorithms that minimize system response time and prevent randomness in displaying data fields to an operator. By employing asynchronomous, prefetching algorithms to load incoming images into RAM, images can be immediately displayed to an operator, thus keeping idle time to a bare minimum. Once the operation is pipelined, not only does it increase operational speed, it allows data fields to be batched and presented to operators on a field basis rather than by the document, which prevents the random display of data that occurs when images that fail data validation are distributed in a document-by-document manner. Keying data images continuously, in the same order in each batch, allows an operator to establish a natural typing rhythm. Intelligent caching of data field images in RAM. Intelligent caching of fully uncompressed field images in RAM allows a data entry operator easy, quick, forward- and-backward access to queued images. This allows a user who senses they typed an error to revisit the data field in question by toggling directly to it in one keystroke, displaying the error, and then correcting it. Seven to ten images in each direction is the practical limit for most operators. Multiple, user-defined, hierarchical ICR editing structures. A sophisticated, hierarchical editing system that includes character correction, data validation, and cross-field verification routines all 6

7 running simultaneously and in background can significantly diminish the substitution error and field reject rate on forms. Running validation and verification routines in parallel prevents a field from being kicked back to operators a number of times for different reasons. The same verification procedures can also be used to trigger manual administrator overrides. User-defined, self-auditing control procedures and automated workload distribution. >Audit control procedures detect and measure the time consumed by each piece of a particular forms processing operation from registration and deskew to noise cleanup, forms removal, hand- and machine-print recognition, and data validation and verification. These procedures are coordinated with routines that regulate the queue volume and control the disbursement of rejected field images to each workstation, which means that each human operator receives a workload tailored to his individual work capacity. Pipelining and parallelizing image transfer components. High production, automated forms processing systems optimize KFI ergonomics to address the throughput objective of keeping the data transfer speed of the imaging pipeline from falling below the speed at which the data images are scanned into the system. Techniques include optimizing, pipelining, and parallelizing image transfer components. Distributing data entry tasks according to keying complexity. Correcting single highlighted characters in a reject-reentry queue is a simpler task than keying in whole fields that fail validation altogether or fall outside the recognition zone of a document, because they usually require knowledge of the applicable business rules. Batch preloading of data entry tasks enables the division of data fields into separate sets that can be distributed to operators according to their knowledge and skill sets. Data fields that the correction interface cannot handle, or that fail validation as a result of knowledge errors on the part of the person who filled out the form, require both knowledge of the underlying business process and the ability to query external databases to enable data fulfillment. These fields may have to be handled by completion operators with specialized knowledge. Moreover, operators must be allowed to force through data fields that cause them to think too long, thus breaking their keying rhythm. Require data entry operators to rekey the entire field if the number of flagged characters exceeds predefined limits. Generally speaking, if a rejected data field contains more than two highlighted characters, the system should be set up to force the operator to key the entire field again, instead of keying in the individual characters. Studies have shown that typing an entire 7-character field at top speed instead of individually typing three randomly highlighted characters in the same field, can be up to 30% faster. User-defined, simultaneous display of multiple editing windows. As previously mentioned, key entry operators in a heads-down operation usually look at least four fields ahead of the one they re currently keying. They need the option of being able to look immediately backward as well, in order to correct self-detected errors. These conditions can be emulated by displaying multiple images of rejected character fields, in tiled or cascaded fashion. Other options include a split-screen view of the entire page to the left, with close-up views of selected fields on the right. The order of keying the data fields should be user-definable, so that each key operator can determine the sequence that permits optimum data entry performance as each operator does in a heads-down operation. Creative character display techniques that enable quality assurance. Sliding-window ribbon editors that focus the eye squarely on the characters that have been designated as ambiguous, are a type of window that makes it easy for the human eye to locate each ambiguous character rapidly. Additional display options include some patented techniques of displaying rows, data fields, and individual characters all-at-once, an arrangement that facilitates ICR quality assurance in a manner 7

8 that is impossible to replicate in a paper environment. The operator can toggle from one mode to another in a keystroke. Other quality assurance methods provide the option of automatically locating and then simultaneously displaying images of all instances of a given keyed-in character (for example, all those typed as a 4 ) in a given document on a one-page display, which makes any error stick out like a sore thumb. Use of eye-friendly colors. To preserve healthy eyes, screen design should follow these general principles. (1) Color choices should ensure readability, by providing adequate color contrast between foreground data and the form background. In other words, no garish colors (such as hot pink, florescent orange) because they tire the eyes after a few minutes. (2) The number of different colors on a screen should be kept to a minimum to avoid a cluttered look. (3) Multiple colors should only be used as indicators of different types of information displayed. In order to ensure compatibility across different monitors and video drivers, use no more than 16 colors. By implementing the above capabilities and procedures, some vendors claim that operators can attain KFI and ICR correction speeds of 12,000 to 15,000 characters per hour rates that rival those speeds attainable by the more experienced operators in a traditional heads down, key-from-paper, forms processing environment. As part of a bigger picture, vendors optimize ICR ergonomics to maximize key entry speed and address the objective of keeping data transfer speed throughout the imaging pipeline from falling below the speed at which the data images are scanned into the system. Their techniques include optimizing all image transfer functionality even rewriting conservatively configured scanner drivers to push scanning speed to the limits of the scanner s optical-mechanical potential. Accordingly, users who focus on data entry ergonomics will succeed in increasing the key entry speed and bottom- line throughput of their operators which, at the end of the day, translates into greater data entry productivity. After a forms automation system is successfully up and running, each member of the data entry staff can be redeployed to work at a task that requires more knowledge than their previous job or at least subjects them to a far lower risk of incurring repetitive strain injuries. In other words, forms automation software should no longer be considered the subject of a missionary sale or a risky investment. Rather, it should be perceived as a low risk, high technology solution that, when implemented, can significantly improve data entry throughput while creating a competitive advantage for the end user. 8