Smarter Document Capture

CVISION Technologies 118-35 Queens Blvd, 14th Floor | Forest Hills, NY 11375 | +1 866-871-7340 | Email: info@cvisiontech.com
This white paper details specific benefits of
OCR, data capture, ways to prepare for success,
and tools to accurately determine ROI.
Smarter Document Capture
Control, Increase, Obtain the ROI
White Paper

Executive Summary:
This white paper details the specific benefits of data capture, ways to prepare
for success, and tools to accurately determine ROI. It provides a comprehensive
understanding of the technology as well as the steps to properly introduce it into
an organization. Paying attention to these critical aspects of a data capture integration
has helped companies meet and exceed their automation expectations.
Recognizing the value of data capture technology is easily measurable. The ability
of the technology to reduce the cost and risk associated with paper-based operation
can be obvious after a single demo. However, placing a dollar figure on the value and
maximizing the success can be much more difficult. There is a substantial number of
variables that can influence a successful integration of data capture technology for an
organization. Therefore, it is necessary to utilize specific techniques and practices to
give data capture technology the best chance of success.
What Is Data Capture:
Data capture is the process of collecting data from paper or image format documents.
Data is typically collected in field formats and stored in a database. The purpose of
data capture is to replace the manual entry of documents and expedite the process
by utilizing computer-processing power. The steps in data capture are input of image
files, document classification, location of fields, extraction of data in those fields,
quality checking of the extracted data, and finally the export of the data in a desired
format. There are two types of data capture: fixed forms processing and semi-
structured forms processing. There is some overlap between these two types, but
the primary difference lies in the setup and configuration of the system. Fixed forms
processing uses static coordinates on an image to find fields, while semi-structured
forms processing uses relative coordinates and context.
Uses - Identifying the Value:
The uses of data capture vary across different document types and business
processes, with some areas having a much greater demand. Organizations
determine data capture needs based on the cost of doing business with certain
document types. Below is a list of the top seven uses of data capture. This list
does not take into account the number of installations for each type of use, only
the need to use data capture.
1.) Accounts Payable ( AP ) Automation
The most common document type found in AP automation is commercial
invoices. AP processing also includes checks, purchase orders, remittance
stubs, and occasionally bills of lading. The ability to automate these documents
means not only a reduction in their manual entry cost, but also two other major
benefits: speed of entry and employee time optimization. Because organizations
can enter these documents faster, they can reduce the time it takes to process
them, and in many cases can reduce penalties or be able to take advantage of
net discounts. Because the operators who typically enter these documents are
not data entry clerks but employees at a higher pay scale, automation allows
them to spend more time on critical thinking tasks.
The purpose of data capture is to replace the
manual entry of documents and expedite the
process by utilizing computer-processing power.
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Control, Increase, Obtain the ROIAutomating EOBs allows billers to retrieve the
money owed to them more quickly. EOBs are
classified as the most complex document type to
automate and because of their complexity, they
also have the highest premium cost for entry.
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2.) Medical Billing
The most common document types in medical billing are Explanation of Benefits
( EOB ), Health Care Financing Administration Forms ( HCFA ), and Universal Billing
Forms ( UB ). EOBs are sent by payers and processed by billers, whereas HCFA
and UB documents are sent by billers and processed by payers. These documents
represent the two directions of the billing process within the health care industry.
Automating EOBs allows billers to retrieve the money owed to them more quickly.
EOBs are classified as the most complex document type to automate, and because
of their complexity, they also have the highest premium cost for entry. Hospitals and
pharmacies regularly receive multi-patient EOBs which are commonly hundreds of
pages long. Automating HCFA and UB documents allows payers to enter the
procedures that are associated with their customers’ claims into their systems
more quickly. This allows them to access the amount owed faster, reduce
operation cost, and increase margins. Occasionally, billers and payers will
process both documents at the same time for purposes of reconciliation.
3.) Survey Entry Automation
While documents requiring hand-print processing ( ICR ) and mark-sense
processing ( OMR ) represent a much smaller paper volume than the majority
of the documents an organization encounters, they are still a common tool for
collecting feedback, testing, or surveying of specific groups. Since each field
on every page is in the same location, survey automation requires fixed form
processing. Because of this, organizations can adapt survey entry automation
technology quickly without much additional effort. The challenge of automating
these documents is in the nature of hand-printed text. Handwriting is constantly
changing. It varies from one individual to another, and sometimes even for the
same individual over time. Because of this, it is necessary to have a properly
designed form in order to constrain the writing as much as possible. Automating
these forms reduces the cost of entry even with the mandatory step of quality
assurance, and is usually much more accurate than manual entry.
4.) Bill Automation
While similar to AP processing, it differs in the fact that corporate bills for utilities
tend to be very different from commercial invoices, and typically have their own
business process, value, and method of processing. Bill processing, unlike AP
automation, does not combine document types. The most popular form of bill
processing is the processing of telecommunication bills, bills generated by phone
utilities for phone and cellular service. For large organizations, these bills create
large avoidable expenses, and can quickly get out of control. Processing tele-
communication bills requires a specific setup of the data capture system. These
bills usually contain multiple pages of repeating data elements. Organizations
require different information depending on their business process. For most
organizations, circuit level data (the highest level of detail on a telecommunication
bill) is not necessary, although for some it is essential. Like other bill types, the
automation of these bills is an opportunity for companies to take advantage of
NET discounts, pay bills on time, and reduce entry cost. For telecommunication
bills specifically, automation helps manage usage fees more closely, and increases
an organization’s ability to negotiate service plans.

The primary purpose of automating mortgage
documents has been to classify these pages
so that they are suitable for automatic filing.
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5.) Mortgage Documents
The property closing process is filled with a large variety of critical documents.
For banks, mortgage companies, and title insurance companies, the information
contained within these documents is critical to ensuring that a loan is properly
structured and complete. While these packets contain the same information for
the most part, their format varies greatly, and they often include special information
based on the situation or state of a loan. The primary purpose of automating
mortgage documents has been to classify these pages so that they are suitable
for automatic filing. For certain document types, organizations will take the
additional step of getting information, usually loan or property details. Automating
these documents makes the entire process of recording and monitoring loans
substantially easier. Before automation was used, these documents generally
were not keyed, but stored in physical storage and retrieved on an as need basis.
With automation, the search and retrieval process can be instantaneous and the
need for storage is reduced.
6.) Logistics Documents Automation
The automation of delivery documents may be a part of AP automation or it may
be its own distinct process. The documents in this category include bills of lading
( BOL ), packing slips, and packing lists. While packing slips and packing lists are
sometimes identical, they can occasionally come at different stages in the shipping
and receiving process, and obtain separate values. The purpose of manually
checking these documents is to verify that the contents of a shipment are accurate
or to inform the receiving organization’s downstream processes associated with
new deliveries. Automating these documents allows staff to focus less on the
details of each shipment, which increasing their ability to monitor the overall
shipping process and increase efficiency. In addition, automation may allow
organizations, typically manufacturers, to initiate faster downstream processes
associated with part deliveries.
7.) Human Resources
Human resource departments have a large range of document types that can
be automated. The majority of the documents that a human resource department
faces are one-off, new-hire documents that are too low in volume to warrant the
use of technology. These documents, which consist of hire forms and resumes,
are not usually automated. There are times, however, when organizations have a
large volume of paper forms due to changes in employment policy, insurance
plans, HR compliance, or just as a result of employee surveys. Because it is HR
staff’s responsibility to enter these documents, the entry time detracts from critical
HR tasks. By automating these documents, organizations get instant value from
them while saving precious staff time. In the cases of policy or insurance changes,
these documents also pose a legal risk value, making time-to-entry critical.

Data Capture Approaches – Best Practices to obtain value:
There are several ways for data capture applications to classify pages, find fields, and
extract data. All data capture packages employ Optical Character Recognition (OCR),
Intelligent Character Recognition (ICR) for hand-print, Optical Mark Recognition
(OMR), and barcode technologies to recognize information on scanned images. The
methods used to classify and locate information vary slightly from package to package,
but there are four primary approaches: templates, iterative templates, data keyword
pairs, and a combination of iterative templates and data keywords pairs. For the
purposes of this discussion, templates are static and definitions are dynamic.
1.) Semi-Automated or Assisted Capture
Semi-Automated data capture is the perfect solution for companies that are
cautious about document automation, or want to start out slow. Semi-automated
data capture is still a manual process. The goal is to make the operator using the
software as efficient as possible by clicking on text versus typing it. Operators scan
documents, view each page and click-enter each field in order. Operators choose
field lists per document type. As they click-enter, the software automatically
navigates from one field to the next. Assisted capture solutions do not require
special expertise during integration and can be installed and configured in hours.
Even if an organization automates only one field with assisted capture and manually
enters the rest, there is a time savings. Assisted capture solutions are less expensive
to purchase and install. Additionally the ROI calculation for semi-automated systems
may not be as high as full automation but it is easier to control and calculate. To give
an example of the savings; an average data capture field is twelve characters in
length. In assisted capture this means that, on average, per mouse-click an
operator enters twelve characters of data, which is a total savings of eleven manual
operations per field. The typical integration of semi-automated data capture reduces
full-time employees needed for data entry by three to five times (AIIM e-Doc 2008).
Additionally because an operator is seeing every image and character the process
has equivalent accuracy to double-blind data-entry in half the time. Organizations
are able to integrate assisted capture in days and start obtaining the benefit from
document automation immediately. When organizations have obtained success
through assisted capture, most are able to easily upgrade to fully automated data
capture approaches. Multiple solutions allow for the template logic created during
the use of assisted capture to be ported to a fully automated system. Organizations
are choosing assisted capture instead of full automation so they can introduce
automation sooner with less risk. Assisted capture provides a level of predictability
and obviousness that fully automation solutions do not. One of the greatest challenges
of automation technology is how the technology fits into the organization. With
semi-automated data capture the impact of change is minimal and usually just a
process of educating manual key entry operators on how to click on data versus
type. Because of this, organizations use assisted capture as not only a way to
quickly automate but also to prepare their environment for automation. Assisted
capture solutions demonstrate similar capabilities in export and quality assurance
to fully automated solutions. In the last two years if an assisted capture solution
has a clear upgrade path to full automation it has been chosen over competing
full automation solutions sixty percent of the time ( AIIM e-Doc 2008 ).
All data capture packages employ Optical
Character Recognition (OCR), Intelligent
Character Recognition (ICR) for hand-print,
Optical Mark Recognition (OMR), and barcode
technologies to recognize information on
scanned images.
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Control, Increase, Obtain the ROITo find the invoice number on an invoice page,
the logic would start by looking for the words
“Invoice No.”, “Invoice Number”, “Invoice #”, or
any other similar phrase on the page.
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2.) Templates
‘Templating’ is the process of picking zones on a document where information
is statically located. The template approach is a fully automated solution. In this
approach, the software does not guess where information is located; it simply
always looks in the same x, y, (height, width) location for each individual field.
These fields are usually defined in the software through a process of viewing a
sample image and rubber banding field locations (drawing rectangles on the
image through the software’s user interface). These locations are then stored as
a template and applied during processing to images of the template type. Templates
are only used in fixed-forms processing data capture.
3.) Iterative Templates
The iterative template approach was a technology that came out of manual
zoning. This approach does not require any more expertise than templating.
With this approach, there is a phase of training that follows these steps. First,
an operator creates a new definition for a document type. He or she then loads
a set of samples that represent that document type and the variations within it.
The operator must iterate over each page in the training set and rubber-band the
same fields. As the operator goes from image to image, the software calculates
the variations in field location from page to page. By doing so, the software
understands how a field may move from one location to another on the page.
Once the training is done, the definition can be applied in production. This
approach is employed by semi-structured forms processing systems.
4.) Data Keyword Pairs or Semi-Structured
The Semi-Structured or data keyword pairs approach is quite unique compared
to all other approaches. In this approach, coordinates are very rarely considered.
If they are considered, they are relative to the subject page being processed at any
given time. In this approach, there is also a stage of training where an operator
loads a set of sample images of a particular document type. The operator then
analyzes the variance across the images and determines the best logic to locate
fields. Most fields are located using keywords. Fields can also be located using
graphics, lines, and white spaces. Once a keyword has been identified for a
particular field, the operator provides the logic to tell the software where the field
is located relative to that keyword or object. For example, to find the invoice
number on an invoice page, the logic would start by looking for the words “Invoice
No.”, “Invoice Number”, “Invoice #”, or any other similar phrase that appears on
the page. Now that the keyword for the invoice field is found, the next step of logic
is to tell the software that the invoice number is to the right of the keyword, some
number or percent of pixels below the top of the keyword, or some number or
percent of pixels above the bottom of the keyword. In this case, the logic would
probably also specify the type of characters an invoice number will contain. In this
approach, the software guesses where the information is located and picks the
best guess if there are several. There is a tug of war in the setup stage of this
approach between how flexible the logic should be and how constrained it should
be. The amount of flexibility is determined by the variance across pages and the
complexity of the document type. Flexibility is usually determined at the field level.
For example, in most commercial invoices, the total amount due is located on the
bottom right portion of the last page of the invoice.

5.) Combination
Some data capture packages will combine iterative templates and data keyword
pair approaches collectively as one or as a choice to the operator based on
document types and the setup operator’s skill set. Other solutions that are fully
automated will incorporate the assisted capture approach for quality assurance.
There are some packages that deviate slightly from these three approaches, but
some variation of them are present in all released packages.
Preparing for Data Capture – Framing Success
The information above explores several uses of data capture that represent the vast
majority of documents being automated. There are other document types not
mentioned that are less common but also benefit from automation, and are in some
respects similar to one or more of the types mentioned. There is no question about
the value of automating the entry of paper documents. Computer processing is
cheap, accurate, and stable. Human labor is expensive and slow. It is easy to see the
value of automation for any organization; the trick is in preparation to automate. The
difference between successful data capture projects and the unsuccessful ones very
rarely has to do with the technology itself, but rather has to do with the amount of
preparation done by the organization to secure success. Organizations that take the
proper steps to prepare for the induction of data capture technology have a greater
success rate and ROI, as well as fewer surprises.
Needs Analysis
All large projects kick off with a needs analysis. In the needs analysis phase,
organizations develop their wish list of automation capabilities to apply to their
documents. For most organizations automating paper, this is a request to automate
one particular document type associated with a single business process. It is
recommended for organizations to initially pick a discrete collection of document
types that are a part of a single business process. Ideally, the document types will
be of moderate organizational risk, and have a high value associated with their
automation, meaning automation will provide an ROI but not change dramatically
how things are done. Later, as the organization learns how to automate, it should
move on to higher risk projects, yielding even higher rewards. When an organization
knows what documents it wishes to automate, it can start collecting the critical facts.
All of these steps happen before any vendor selection, and before any testing of
technology takes place. The objective is a better understanding of the need before
any investigation of technology.
Preparing Sample Sets
The sample documents an organization uses to evaluate technology is the most
important tool for gauging potential value, measuring exceptions, and ultimately
picking a solution. The biggest mistake that is made is to not formalize the process
of picking samples. Without a well-prepared static sample set, there is no consistency,
which in the end diminishes the value of the sample set and testing period. Because
this white paper deals with such a range of document types, the exact calculation
of sample set quantities and variance is not specific. The following page contains a
general guide to make organizations aware of the elements they should consider.
There is no question about the value of
automating the entry of paper documents.
Computer processing is cheap, accurate, and
stable. Human labor is expensive and slow.
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Control, Increase, Obtain the ROIThe purpose of the truth data is to compare
the prospective products’ recognition results
to already known, 100% accurate, manually
entered data.
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1.) Sample Set
The sample set is the collection of already imaged documents on which the
prospective software packages are configured. Sample sets should consist of
a fixed number of each document type the organization plans to automate. For
example, if it includes AP processing of invoices, purchase orders, and checks,
then there are three types. An organization should have no less than ten sample
production documents per type. The documents should be exactly as the data
capture software will receive them at its final integration. The number of samples
will be scaled based on production volume, but should not exceed fifty per type as
quality analysis then becomes unbearable. Each type should contain as much or
as little variation as is experienced in the production environment. If, for example,
an organization processes a thousand commercial invoices a month and has
a thousand separate vendors in its system, each sample invoice should be from
a different vendor. But if an organization processes the same volume from only
three separate vendors, then there should be several samples of each and more
for the greatest two contributors in volume. Because organizations are sharing
private data they should take the proper measures to protect themselves. If an
organization must sanitize documents before providing them to any vendor, it
should not black-out (redact) information it expects the data capture system to
collect. The best option is to substitute real information with fake, as redaction
could impact the technology evaluation process. The above sample set is ideal
for demos, and estimating value; for a proof of concept, the sample set needs
to be revamped.
2.) Production Sample Set
Production sample sets are the samples that are run through the prospective
software packages after setup has been done on the above sample set. The
production set should be two times the volume of sample sets and have exactly
the same variational makeup. The reason the software is tested on an independent
sample set is to best approximate the production environment and to isolate any
effects of setup on static documents.
3.) Truth Data
Truth data is the 100% accurate, manually entered data for a given set of
documents. While truth data should ideally be prepared for both the sample set
and the production sample set, many organizations will evaluate accuracy at the
point of proof of concept, so truth data for the production sample set may be
sufficient. The purpose of the truth data is to compare the prospective products’
recognition results to already known, 100% accurate, manually entered data.
4.) Evaluation Method and Criteria
Organizations need to agree internally on the method that will be used for testing
products and how they will be measured before any actual testing is done. The
recommended methods for most organizations’ needs are listed below.

The organization needs to focus on the benefit
of the technology and understand from the
vendor the amount of preparation and skill level
required based on work that has been done.
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1.) Vendor Discovery
At the first interaction with the vendor, organizations should remove any
potential deal killers such as the pricing model or support concerns. The
organization needs to focus on the benefit of the technology and understand
from the vendor the amount of preparation required and the skill level required
based on work that has been done on the above sample set.
2.) View a “canned” demo of each prospective product.
Canned demos are pre-configured demonstrations of the software on
vendors’ picked sample documents. These demos do not require the
vendor to perform any work other than presentation of the demo.
3.) Modify the prospective vendor list based on the demos
4.) Have vendor perform setup on the sample set
5.) See demo of each prospective product on the sample set
6.) Modify the prospective vendor list
7.) Begin price negotiation
8.) Obtain a trial from remaining vendors with tailored configuration for
the sample set.
9.) Run the production sample set through setup of the final prospective
vendors’ products.
At each step, organizations should evaluate the speed of creation, speed of
processing, and accuracy. Using this method in conjunction with the above facts,
the organization should end up with a vendor list and associated performance
score for each vendor based on the organization’s needs and expectations.
Understanding Accuracy and Creating Expectations
Data capture accuracy is often confused with full-page optical character recognition
accuracy ( OCR ), which has a single percentage of error based on the number of
correct characters. This confusion can cause many problems when an organization
is determining the system for evaluating data capture products. Data capture accuracy
is derived from a series of accuracy calculations. The calculations go step-wise, and
each step impacts the next; failure in one step often prevents the other accuracy
levels from being calculated. The best way to consider the accuracy of a package
is to measure both the actual accuracy achieved based on truth data, and based
on the percentage of uncertainty. Uncertainty is the percentage of characters a
software package flags for a manual review. If there are one hundred characters in
a document and the percentage of uncertainty is five percent, then an operator will
look at five characters. In data capture, it is important for organizations to understand
that despite the reported accuracy rate, there is always a potential for false positives.
A false positive is a result that technology reports as accurate, but in reality is not.
False positives are combated during setup with business rules and data types.
Below are the stages of accuracy calculation.

Control, Increase, Obtain the ROICharacter Level OCR is the most commonly
known and used form of measurement.
Character level accuracy ranges from zero to
one hundred percent accurate.
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1.) Page ID
The identification of a page and its associated document type. If a page is not
identified as any type the accuracy is zero percent; if the page type is accurately
identified, it’s accuracy is one hundred percent. Pages identified as a wrong type
( false positive ) result in zero percent accuracy. Page ID is an all or nothing
accuracy calculation. A zero percent accuracy may stop processing of a
document entirely or result in false positives.
2.) Field Location
This is the process of zoning the fields to be recognized on the document. If a
field is not located at all, it is zero percent accurate. If a field is partially located, it
is one to ninety nine percent accurate, depending on total possible field length and
the length identified.
3.) Character Level OCR
This is the accuracy reported by the OCR engine per character. When referring
to accuracy, Character Level OCR is the most commonly known and used form
of measurement. Character level accuracy ranges from zero to one hundred
percent accurate. If a field has ten characters and nine are correct, the field is
ninety percent accurate.
4.) Business Rules and Data Types
This accuracy changes based on different software packages, but is applied
at the final step of recognition. If a field does not match a particular data type,
for example a proper date format, it could impact the accuracy of that field by
one hundred percent, or reduced by a percentage representing the number
of characters of the whole that does not match the data type. If a business rule
states that a particular field should be five characters long, and it is recognized
as seven characters long, it could impact the accuracy of that field by one
hundred percent or less. Business rules and data types are the final tools
to enhance accuracy and avoid false positives.
All four of the above accuracy calculations can be rolled into a single percentage
that becomes the final calculation of data capture accuracy per page. Production
environment accuracy is usually determined by the running production of documents
for a set period of time and averaging the per page accuracies. Companies dealing
heavily with business process driven automation consider accuracy only on a
complete document level as opposed to page level. For example, consider AP
automation of invoices, PO’s, and checks. There may be an average page level
accuracy of ninety five percent on the invoices, ninety five percent on the PO’s
and only seventy five percent on checks and because a document is only as
strong as its least accurate type, the accuracy of AP automation would be
considered to be seventy five percent on average.

Organizations have been surprised that while
they obtain tremendous ROI when they
initially use data capture, the rate at which
ROI decreases is tremendous due to poorly
planned exception handling.
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Bringing the Team Together
With the above metrics and tools to evaluate software packages, organizations can
move to the next important step, which is building the team responsible for integration
and users of it. History has shown that one of the biggest determinants of success
in data capture projects has been the level of understanding and acceptance of the
stakeholders involved. Bringing new technology into an organization impacts the way
processes are executed and can have surprising effects. The best way to mitigate the
stress from the change is to build a team of constituents and start educating them
about the technology and its induction. This process will yield natural feedback that
will likely spawn an additional refinement of understanding.
Setting Goals
With all the above tools, an organization can now estimate the levels of automation it
can expect to achieve with data capture technology. In this estimation, an organization
should include detailed answers to the five categories below for determining success.
1.) Desired Accuracy Range
Organizations should be realistic in the accuracy range they expect. The range
should have as a mid-point the estimated accuracy achievable on the subject
document types. The reason this should be the mid-point is because this is the
organization’s biased expectation, and the real accuracy will generally be higher
or lower. Based on our experience with this type of technology, the estimate might
be off and organizations should be prepared to make adjustments. The calculation
of the range is based on the monthly page volume of documents, and the accuracy
it will take to automate the percentage needed to reduce data entry cost.
2.) Exception Documents
Exception documents are documents for which a configuration was unable to
extract any, or was only able to extract minimal, usable data. Exceptions often
mean additional setup and fine-tuning of a configuration. Because of this they
may dramatically impact ROI, as each round of fine-tuning will result in an internal
or external cost. Organizations should isolate this variable and determine an
acceptable range of exceptions. Exceptions will always occur, and the range is
relative to the monthly page volume, variations between document types, and the
expected amount of new variations per month or year. At this time the organization
must also decide the number of times any one exception must repeat before a
round of fine-tuning is considered. It is not advisable to fine-tune for a class of
exceptions that occur once or even five times. It helps organizations to list an
acceptable cost range for working with an exception document type, and to step
into the calculation of the number of fine-tuning rounds permitted in a given time
period. Organizations have been surprised that while they obtain tremendous ROI
when they initially use data capture, the rate at which ROI decreases is tremendous
due to poorly planned exception handling.

ROI ranges are highly dependent on document
types and quality. Most general business
documents of good quality are moderately
complex to automate.
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3.) Technical Ability
Organizations should be aware of the technical ability of the staff appointed to
configure and use the data capture system. Usually, organizations do not have
to consider the operating complexity of the data capture product, as software
companies design data capture products to be simple to operate once they are
configured. What varies more is the setup of a system. The complexity range
that is acceptable is determined by the skill set of the staff designated to set up
and support the data capture system. Some packages offer “what you see is
what you get” or WYSIWYG type setup that only requires personnel who are familiar
with basic Windows application usage. However some packages require a
developer-level of expertise. Most packages have both, and developer assistance
is only needed as one-off support. Setup complexity is important during the initial
integration, though this may be handled by the vendor and during exception
handling fine-tuning. The complexity of a product is not necessarily indicative
of its accuracy.
4.) Processing Speed
There are several stages in data capture processing where speed is an important
consideration. With this type of technology, it is usually the case that slower
means more accurate. Organizations need to make the decision of what speed
of processing, setup, and exception setup is acceptable for their business process.
Speed of processing is determined by measuring how long manual entry takes
per page. Documents entered using data capture should take less time to enter
than if they were entered manually. For some organizations, entry in the same
amount of time is acceptable; for most, it is between thirty to two hundred percent
less time per page. Expectations should be realistic and based highly on the complexity
of the documents. Often, organizations focusing only on speed will pick faster,
less accurate technology, and will not obtain an ROI as there will be more manual
quality assurance checking.
5.) Setup Time
The amount of time it takes to set up and train for expectations impacts the time
it will take to start gaining value from automation. The more time it takes to set
up, the longer it takes to start automating documents. However, generally, the
more setup time spent, the more accurate the system will be. Organizations need
to know the range of time for initial setup that is acceptable. In data capture, the
average is between 3 to 6 months, with outliers on either side for initial setup. The
average time per exception document type can range from minutes to weeks.
Estimating ROI
With the above five answers, a company can now make an initial estimate of its ROI
based on a certain percentage of automation. Expectations change frequently for
organizations not yet experienced in data capture. As organizations become more
familiar with the technology and educated in expectations for their area of automation,
they increase their ability to estimate accuracy and performance. ROI ranges are
highly dependent on document types and quality. Most general business documents
of good quality are moderately complex to automate. Examples of complex document
types are EOBs and student transcripts, which require a substantial additional fine-
tuning effort. On the easier side are packing slips, and survey forms.

Control, Increase, Obtain the ROIIn assisted capture, the calculation of ROI is
fairly basic and straight forward. It’s a process of
counting the number of user operations that are
saved with click-entry versus manual key entry.
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Organizations should be mindful of the formula they use to calculate ROI, and repeat
that formula during the evaluation of each product at each stage even as expectations
change. Usually, data capture ROI is determined by how much money is saved in
automation. There are, however, many other areas for organizations to gain ROI from
data capture automation that should be considered. For example, automation often
frees employee time to be spent on more critical tasks, thus making them more
efficient. When an employee is more efficient, less staff is necessary, which decreases
staffing costs. Another example is the reduced cost of paper storage. Because of
automation, some organizations have a lower need to physically store paper, which
reduces monthly storage fees. For some particular industries, ROI is based on a
reduction of risk associated with compliance, or even a reduction of legal fees such
as worker’s compensation claims due to manual entry.
In assisted capture, the calculation of ROI is fairly basic and straight forward. It’s a
process of counting the number of user operations that are saved with click-entry
versus manual key entry, then calculating what volume of savings is required to
replace the work of one operator. As an organization’s paper volume increases,
so do the savings. The easiest way would be to calculate how many pages can
be entered manually compared to click-entered. This gives you a percentage in
terms of time savings. Below is a basic sample calculation.
Assumptions:
Average Operator Hourly Wage: $8.00
Average Document Content: 33 fields of 12 characters
Average Operations Per Operator Per Hour: 11,600 ( Data Entry Management
Association 1998 )
Average Number of working days per month: 22
Average Number of working hours per day: 8 hours
Average Cost of Semi-Automated License: $6,600 one-time, $1,100 annual support
Average Professional Services for Semi-Automated Solution: $10,000
Calculating for 100,000 pages a month data entry:
Manual Entry
In an 8 hour day a part time employee will enter approximately 234 ( 11,600 key-
strokes / (33 fields x 12 characters) x 8 hours) pages a day or 5,148 (234 pages * 22
days ) pages a month. It would take 20 ( 100,000 pages / 5,148 pages ) full time
employees ( FTE ) to handle the monthly data entry volume for a total cost of
$28,160 ( 20 employees x 8 hours x 22 days x $8.00 ).
Total Manual Monthly Cost: $28,160

13
Assisted Capture
An operator performs an average of 34 clicks ( one per field and one for template
selection) and an average of 10 keystrokes ( verification of data ) a total of 44
operations to enter a page. The operator can then click-entry 263 ( 11,600 key-
strokes / 44 operations ) pages an hour, 2,109 ( 263 pages x 8 hours ) pages a day,
or 46,398 ( 2,109 pages x 22 days ) pages a month. It would take 3 ( 100,000 /
46,400 ) FTEs to click-entry the entire monthly volume for a total cost of $4,224 ( 3
x 8 x 22 x $8.00 ).
Total Semi-Automated Monthly Cost: $4,224
Monthly Savings of using Semi-Automated: $23,936
3 Year Total Cost of Semi-Automated Solution: $36,400 ( ($6,600 one-time + $1,100
annually x 2) x 3 licenses + $10,000 services )
Return On Investment: 1.5 months ( $36,400 cost / $23,936 savings ).
Preparing and Evaluating a Vendor List
Now that the majority of the fact gathering is done, organizations can prepare the
vendor list. Initially, they should be broad and look for prospective vendors that fit
just the type of processing in question, either fixed or semi-structured document
processing. Vendor lists should include the vendor name, their contact information,
and the product they have that might be a fit. Organizations should set the number
of iterations and evaluation criteria that are used for each vendor based on the above
research. Once there is a completed vendor list, the evaluation process can begin.
Because of the nature of the technology, there are several areas where vendors
should be tested thoroughly.
1.) Do They Provide Demos?
Vendors should give organizations the option to first see a canned web demo of
the product in question. Once a canned demo has been given, vendors should
accept sample document sets and be willing to perform some basic setup on
the samples. When this has been done, seeing a demo based on the organization’s
sample set will clarify the potential of the technology. Many vendors have websites
where organizations can simply upload documents to test. When evaluating the
demo with the sample set of documents, organizations need to understand the
complexity of the setup that was done. How long did the vendor take to do the
setup? What was the skill level of the person who performed the setup? Finally,
what were the challenges during their setup?
2.) Do They Provide Trials?
Does the vendor allow organizations to try the software for a period of time in a
production environment? Often, the complexity of data capture software can be
such that a trial of the software without guidance or initial setup is more
harmful than good. If this is the case with a particular vendor, they should offer
to do a setup for the organization and provide a trial that is operator mode only,
or clearly explain the skill level required and what one may expect to encounter
when performing setup without training.

Control, Increase, Obtain the ROIThere are many factors that come into play
when integrating data capture technology.
Because of the interpretive nature of this
technology, there are also many nuances to
contend with.
14
3.) How is their Support?
The trial period is an opportunity for organizations to contact the vendor’s general
technical support line. It is important to know if the vendor’s technical support
team is responsive and can answer their questions or escalate requests promptly.
4.) Test the Metrics of the Software
Many vendors have different versions of the data capture product they offer.
Organizations need to find the package that is the best fit for their needs.
Differences in packages are usually associated with the volumes they can process,
and it comes down to cost. During the trial of the appropriate packages, it is the
organization’s responsibility to test all of the metrics: installation, speed of recognition,
accuracy, and export file formats. Export formats are sometimes the primary
focus of an organization. Organizations should not pick data capture products
based on export format but be concerned only that the export gives them all of
the data required to get to the desired format. Sometimes the data capture software
provides not only data but also image file results. If this is the case, then image
format capabilities and resulting file size will be an additional consideration. Many
packages include compression and file tools that offer companies the ability to go
directly from a data capture process to an Enterprise Content Management ( ECM )
or Document Archive System without any additional steps.
Best Practices to Improve Success
There are many factors that come into play when integrating data capture technology.
Because of the interpretive nature of this technology, there are also many nuances
to contend with. Even so, there are some clear ways to make the integration of data
capture technology more accurate. Below are some of the primary influences on data
capture accuracy that all organizations should consider.
Form Design
The way in which forms are created can dramatically impact the data capture
accuracy when being processed and scanned. Organizations that have control
over the creation of their forms are in the best control of this factor of accuracy.
The best practices for printing forms are based on fixed or semi-structured
types. The most control can be gained on fixed forms, and thus the greatest
impact, but semi-structured typographic forms also have potential for improvement.
Fixed Form Design
1.) Cornerstones
Make sure your form has corner stones in each corner of the page. The corner
stones should be at 90 degree angles to each of their neighbors. The ideal type
is black 5 mm squares.
2.) Form Title
A clear title in 24 pt or higher print that does not use a stylized font.
3.) Completion Guide
It is optional but sometimes useful to print a guide on how to best fill in the fields
of the type you use at the top of the form.

15
4.) Mono-Spaced Fields
For the fields to be completed, it is best to use field types that are character-by-
character separated. Each character block should be 4 mm x 5 mm and should
be separated by 2 mm or more. The best types of fields to use in order are letters
separated by dotted frames, letters separated by drop-out color frames, and
letters separated by complete square frames.
5.) Segmented Fields by Data Type
For certain fields, it will be important to segment the field in portions to enhance
ICR accuracy. The best example is date: instead of having one field for the complete
date, split it into 3 separate parts, the first being a month field, the next a day field,
and the last a year field. The same is done with numbers, codes, and phone numbers.
6.) Separate Fields
Separate each field by 3mm or more.
7.) Consistent Fields
Make sure the form uses consistent field types.
8.) Form Breaks
It is okay to break the form up into sections and separate those sections with solid
lines. This often helps with template matching.
9.) Placement Field Names
This is for the text that indicates what a field is, such as “first name” or “last
name”. It is best to put these left justified to the left of the field at a distance of
5mm or more. DO NOT put the field descriptor in dropout in the field itself.
10.) Barcodes
Barcode form identifiers are useful in form identification. Use a unique ID per form
page and place the barcode at the bottom of the page at least ten mm from any field.
Semi-Structured Form Design
1.) Spacing
Provide sufficient space in each field for data to be entered.
2.) Limit Use of Lines
Text can often be printed on lines and this is problematic, no matter which
technology or imaging tool is used.
3.) Field Names
Print field labels to the left of input text. It’s best not to allow input text to be
below field labels, as the field label then often interferes with OCR.
4.) Effective Dropout
When using dropout, make sure the form has some black-only elements. If all
referencing elements on the form drop out, the data capture software has no
reference points to find even the first field. It’s best to have field names as black
text that would show up in a scan.

Data capture is valuable to any organization
with paper. Data capture reduces data entry
cost, and enhances the value that paper
documents provide an organization.
16
Scan Prep
Preparing documents to be scanned can be a very time consuming process, but
improperly prepared documents are problematic to any data capture process. Single
page document preparation is the easiest to achieve. When dealing with document
types that include samples, folded pages, and re-ordering, it is important for the ac-
curacy of preparing these documents to be high. Most data capture applications are
very good at rotating pages; this may be an area where document preparation time
can be saved. Double page scans are the most problematic because of staples or
folded edges, as well as skew because of improper document feeding.
Scanning
Proper scan settings are absolutely critical to obtaining the highest level of accuracy
in data capture. While there are many scan settings that are based on document
type, there are a few ways to ensure that all documents are scanned properly.
1.) Resolution
The optimal resolution at which to scan documents for data capture is 300 DPI.
This setting is optimal for accuracy and speed of scan. Companies working with
documents with small font or hand-print may consider scanning at a higher
resolution, but this is rare.
2.) Color Scanning
To ensure that the data capture software has the greatest possible amount of
information to work with, organizations should scan in color. Often, organizations
will pick a lower bit depth, considering only file size. Scanning in color will help
obtain the highest accuracy and is a format that can be compressed and re-purposed.
3.) Image Pre-Processing
Occasionally after a document scan, image preprocessing provides additional
benefit to the accuracy of data capture. The types of image processing should
only be chosen by organizations when necessary and proven to help accuracy.
To do this, an image should be tested both with and without image processing.
The types of image processing that are most beneficial to data capture are
thresholding, despeckling, rotation, deskew, background removal, and correction
of linear distortion.
Putting it Together
Data capture is valuable to any organization with paper. Data capture reduces data
entry cost, and enhances the value that paper documents provide an organization.
For data capture technology to obtain the greatest return on investment, it is the
responsibility of organizations to do the proper planning prior to testing and deploying
this technology. By building an understanding of the internal business processes to
be automated, preparing sample sets, creating evaluation criteria, and preparing for
exceptions, organizations can exceed their expectations. Well-prepared organizations
can increase their ROI and achieve a greater degree of automation than originally
anticipated. Successful integration leads to the introduction of the technology across
other business units and other paper-based processes.

17
About Chris Riley
Mr. Riley is an independent industry expert and founder of LivingAnalytics, Inc.
He specializes in OCR, ICR, OMR, Barcode, BCR, data capture and Analytics
Technologies. He lives and breathes technology and has been helping companies
buy, use, and optimize these technologies given their business process and use
case. Chris’s focus on market education has helped expose underlying problems
in the acquisition and use of advanced technology and has helped both end-users
and vendors mitigate the challenges to achieving ultimate success. Riley has 12
years experience in this arena, owned three software companies, and obtained
several technology and business awards. He has three bachelors degrees in the
areas of Business Administration, Computer Science, and Mathematics, and holds
certifications from the enterprise content management trade organization AIIM as
“Enterprise Content Management Practitioner ( ECMp )” and “Information, Organization,
and Access Practitioner ( IOAp )”. Mr. Riley is a sought after speaker, and educator
throughout the content gathering space.

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