a digitally created printout 10 of this invention is created using a test deck system 12 as shown in fig1 . the print out 10 is sometimes referred to as the digital test deck ™ ( dtd ™), or simply the test deck 10 , and can be used in a handprint recognition system to test the system for accuracy and to improve the efficiency of the forms processing technology . when accompanied by a truth file 14 , which accurately represents the data printed on the form and defines the expected output of the forms processing system , the test deck system 12 produces a very realistic and accurate set of testing forms . the dtd ™ printout 10 can be used for production readiness and for baseline testing of forms processing systems such as shipping labels , bank checks and surveys for accuracy and efficiency . the test deck system 12 for creating the dtd ™ printout 10 includes a file such as an adobe pdf or postscript file that includes instructions to describe an appropriate form 15 . it also includes a page description file ( pd file ) 16 formed by page description software 18 . the pd file 16 may be an adobe quark express file . the pd file 16 contains the instructions necessary to determine what form to use and how that form is to be filled out . the form may be a “ blank ” form 20 ( see fig2 ) that can be directed by the pd file 16 to be sent to a printer 22 which could be a traditional offset printer , a color digital printer , or other types of printers and then prints the page images of the forms in duplex or simplex , depending on the requirement . this test deck system 12 also includes a form description file ( fdf ) 24 that describes the characteristics of the form &# 39 ; s data fields . the fdf 24 contains information such as field length and whether the field is a check box , or a write - in field for marks or characters respectively . field length can be measured in the number of marks or characters . the test deck system also includes a variable data database ( vddb ) 26 that describes the desired content of the simulated respondent entries . the vddb 26 is an ascii database that tells what goes where on the printout . in the case where the field is a check box field , the vddb 26 describes which check boxes are checked and which are blank and the type of mark that is expected . when the field is a write in field , the vddb 26 contains a simulated response , such as a last name , “ smith .” the vddb 26 allows customization depending on the form 20 used and the job requirements . the test deck system 12 shown in fig1 uses handprint character snippets ( hpc ) 28 organized in an hpc snippet database 30 . the hpc snippet database 30 can include characters , letters , symbols , or parts thereof , where a single character is defined by the set of legal symbols for the particular user or job requirement . the hpc snippet database 30 has an appropriate identification / numbering scheme for subsequent incorporation into the test deck system 12 . the individual hpc snippet 28 is preferably an image clip containing a hand printed symbol ( character , mark or punctuation mark ) obtained from a real person . by using the digitally created printout 10 with hpc snippets 28 , as described below , it is possible to test the accuracy of form processing by an optical character recognition ( ocr ) method and / or system with the assurance the results are realistic . optical character recognition is often referred to as ocr or icr ( intelligent character recognition ). optical character recognition refers to the process of automatically recognizing write - in fields from the scanned image of the form . originally , ocr referred to the recognition of machine print , and is used by government institutions , but has also been used as a generic name for recognition of handprint as well . sometimes , the type of ocr used for handprint is called “ handprint ocr .” optical mark recognition ( omr ) is a related process and refers to the process of automatically recognizing the answers to check - box questions from the scanned image of the form . it is a more advanced version of its predecessor which was known as “ mark sense ” which determined if a particular circle or other shape was completely filled in by the respondent . the vddb 26 contains acceptable responses for each data field on the form effectively simulating the data that would be obtained from a real respondent that had completed the form but with advantages that are important to improved accuracy and efficiency mentioned above . the vddb 26 can be created from a dictionary of acceptable data for each field . the vddb 26 is capable of specifying the placement of letters such as upper case or lower case to more closely simulate actual respondent data and / or changing the placement of the handprint character snippet in reference to a boundary by one or more of the handprint character snippet &# 39 ; s position , angle , or size . these letters can extend below the line as if written by hand . these letters include those that have extensions such as g , j , p , q , and y , and can be composed from one or more hpc snippets 28 as will be discussed in detail below . the test deck system 12 shown in fig1 incorporates a hpc snippet database 30 of characters , symbols and / or digital images . the hpc snippet database 30 classifies the characters and / or digital images by similarity and / or a feature set . the hpc snippet database 30 contains all the valid or verified character images which have been collected to date in the project . these can come from one or more different sources and mixed together using a computer . the one or more of the sources can include computer - generated samples . the hpc snippet database 30 works in conjunction with field dictionaries 31 and / or alternatively , with subfield dictionaries , which can be ascii files . this dictionary 31 contains a table of valid entries for any given field on the blank form 20 . when there is a complex linkage between fields in a form , such as between the preparer &# 39 ; s gender and first name , the dictionary can be subdivided into subfield directories . this method creates the test deck used to qualify and test handprint recognition systems by preparing a handprint snippet database containing labeled handprint image snippets ; preparing a form description file and page description file to describe a form ; preparing a variable data database that describes the desired content of the simulated respondent entries using the handprint character snippets ; populating the form using the variable data database in conjunction with the form description file , the page description file , and the handprint snippet database ; and printing the completed form . the test deck system uses a “ hand ” 32 defined as an “ adequate ” supply of characters or symbols , preferably from the hpc snippets database 30 , to create the set of handprint field snippets 33 which satisfy the field data requirements for the vddb file 26 . the process is controlled by the variable data snippet maker ( vdsm ) software 34 . the hand 32 can be a homogeneous hand which is a hand in which all the characters are similar dimensional characteristics and features ( i . e ., slant , line width , height , etc . ), so that the set of handprint field snippets 33 created from this homogeneous hand 32 look as though they were filled in by one individual . vdsm software 34 describes the logic that controls handprint field snippet generation including the production of the set of handprint field snippets 33 that are to be put on the form image 36 of the appropriate form 15 . the vdsm software 34 uses the data field information found in the vddb file 26 and the field description found in the fdf 24 to appropriately select hpc snippets 28 and electronically paste the set of handprint field snippets 33 together . the handprint field snippets 33 are subsequently electronically pasted onto the digital test deck file 38 , using variable data printing software 40 to vary or raster images as is known by those in the art . it is sometimes advantageous to incorporate a random or alternatively a defined noise into the data to simulate certain environmental or expected effects . at the same time the set of handprint filed snippets 33 are used to create the digital test deck 38 , the well - defined truth file 14 is being constructed to contain the “ answer ” expected from the system using the form supplied . the hpc snippets 28 are digitally pasted together to construct the set of handprint field snippets 33 described above . this set of handprint field snippets 33 will be subsequently printed on the printout 10 . the hpc snippet database 30 can be used in conjunction with the vdsm software 34 , and data field information as well as the field descriptions , to create snippets that are placed on the fringe of acceptable units ; such as a snippet touching a boundary or another character , with at least one point . the hp snippets 28 can also be cast into a vector representation if necessary , or placed in reference to a boundary by one or more of the hp snippets . a form image 42 , the page description file 16 , and the handprinted field snippets 28 are processed through variable data printing software ( vdp ) 40 to create the digital test deck ™ ( dtd ™) file 38 . the dtd ™ file 38 is sent to a printer 22 , which may be offset or digital , to produce the dtd ™ printout 10 . this printout and the dtd ™ truth file 14 comprise the system output that is available to the customer for test purposes . the dtd ™ 10 containing simulated human handprint , looks like a form prepared by a real person even through they are printed by a digital printer and contain perfectly known data . using these decks , a forms processing system may be tested for accuracy and efficiency , regardless of the technology used for the data capture . more specifically , the state of the processing system may be reliably assessed at any point in time . a well - defined truth file 14 is also developed by the handprint recognition system using extracted data from the forms to accurately represent the data placed within the dtd ™. the truth file 14 would contain the “ answer ” that is expected from a handprint recognition system , and can be used to determine the accuracy of said system . fig2 shows a blank form , which is a portion of the year 2000 decennial census “ short ” form . this is an example of a form that would benefit from the described system and method . there are places on this ( blank ) form 20 , called fields 44 , where the respondent is asked to print the answers to questions posed by the census bureau . when the respondent completes these fields , the form might look like fig3 displaying fictitious data 46 in fields 44 . most people would say this was an actual census form image , albeit a rather neat one , but it was actually created using a handprint font on a computer . this is one example of a digital test form . a suitable number of different digital test forms would constitute the test deck . the basic properties of the test deck as defined above are : looks and feels like a real form with handprinted responses , but really printed on a high quality digital color ( or black & amp ; white ) printer ; form content designed to test critical system aspects ; reproducible as required ; compliments , but does not replace forms with “ real data ” content ; consistent test input ; test the data capture system “ end - to - end ;” and know the “ truth ” perfectly . the fourth bullet indicates that using handprint fonts results in a rather excessively “ neat ” simulated form , being created , and so the form is of limited use in actually measuring ocr data capture quality per se . however , using actual handprint “ snippets ” 28 in the creation of the test deck 10 gives a very realistic appearance , as shown in fig4 ( again using fictitious data ). verify correct operation of critical system components ; establish a measurable system performance baseline ; test system operation at each software / hardware change ; test daily production operational readiness before scanning ; test consistency of system between scan operations , sites and over time ; verify system “ improvements .” this invention enables “ outside - in ” testing . if a perfectly known input is inserted into the system , and ( mostly ) the correct answers come out , then it is unlikely that there is anything seriously wrong in between . alternatively , “ inside - out ” testing , analyses all possible internal variables such as a measure of the lamp intensity on the left - hand side of the scanner , or the speed of the scanner transport , etc . the problem with an “ inside - out ” approach is that it may literally fill up file cabinets with data in this manner , and it will be the element or factor that is not tested that causes the system to fail or create erroneous data . the “ outside - in ” approach used in this invention is advantageous because testing is simple , cost - effective , accurate , and consistent . how to measure accept rate and accuracy of a forms processing system fig5 shows a typical forms processing system 50 which uses automatic recognition ( ocr ) to do the bulk of the data capture workload , and kfi for data capture of those rejected fields for which the ocr system is not confident . the terms accept rate and accuracy rate are used as a measure of the accuracy of the system under test . in automated recognition of hand printed fields , the accept rate is the fraction of the fields in which the ocr has high confidence , usually expressed as a percent . these “ accepted ” fields are the ones noted for ocr accuracy . accepted fields are not sent to keyers except for quality assurance purposes , while in noting the ocr “ accepted ” fields , that fraction of the ( non - blank ) fields that are “ correct ” is the accuracy rate , usually expressed as a percent . also shown are the steps taken to measure the accept rate and accuracy of the system . finally the error rate for either ocr or omr is defined as 100 % minus the accuracy rate in percent . so for example , if the accuracy rate is 99 . 2 %, the error rate is 0 . 8 %. related to the error rate is the reject rate . the reject rate for recognition is 100 % minus the accept rate in percent . so , if the accept rate is 80 %, the reject rate is 20 %. rejected fields are the low confidence fields from the ocr , and are sent to keyers to be keyed because the automatic ocr isn &# 39 ; t sure it has the correct answer . for omr , the accuracy rate is that fraction of all the check - box fields that are correct , usually including blanks . blanks are commonly included in scoring omr because there is no way for the computer to know if a check - box contains a mark or not without looking at it , and so an empty check - box which is properly identified as such is considered a “ correct ” output of omr . scoring ( also called : an accuracy rate ) includes the calculation of the accuracy of an ocr or keying ( data collection ) system . a truth file 14 also referred to as the ground truth or answer file , contains the set of values expected as output from an ocr / omr system upon extracting the respondent completed information from a form . when the present invention is used this truth file 14 can be generated as described below . a portion of the test deck system 12 is shown schematically in fig6 where all of the fields 44 in all the forms are being tested together . the fields 44 are pulled out one at a time for testing . if the handprint was j - o - s - e and the resultant ascii was jose , that would be a correct field , termed a “ hard match ”, meaning each and every character is correct in a field . on the other hand , if the handprint was c - h - ao and if the resultant ascii was chad , there would be an error using the hard match criterion . ε τ = ε o a o + ε i ( 1 − a o )+ ε τ ε τ = total data error and the estimator is shown with a ^ over the ε τ . fig7 shows a mathematical representation of the results of applying this test deck system 12 . if an accuracy in the neighborhood of p = 99 %, corresponding to an error rate of q = 1 % is needed , then the following relationship approximately describes the one - sigma sampling error in the estimate : where n is the number of samples . fig8 is a plot of the measurement error as a function of the number of samples . using this method , it is possible to determine how many samples are needed to obtain the desired level of quality in estimating the desired system accuracy . fig8 shows that many samples may be needed to test the test deck system 12 properly . this invention makes creating a large number of samples cost - effective compared to previous manual methods . six basic types of test materials are used to test forms processing systems are : 1 . blank forms ; 2 . forms hand - marked by volunteers ; 3 . real forms filled out by respondents ; 4 . images of real forms on cd - rom ; 5 . lithographically printed forms with simulated respondent marks ; 6 . digitally printed forms with simulated respondent marks . each of these types of test materials has a purpose , and has advantages and disadvantages . by a suitable combination of these materials , tests may be devised to cover all testing needs . while the invention has been described in connection with various embodiments , it is not intended to limit the scope of the invention to the particular form set forth , on the contrary , it is intended to cover such alternative , modification , and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims . in particular , the test decks described herein could be comprised of a wide variety of printed forms in addition to questionnaires ; for example , bank checks , shipping labels , and other types of printed forms .