Patent Publication Number: US-2022237937-A1

Title: Distributed computer system for document authentication

Description:
BACKGROUND 
     The present invention generally relates to a method of auditing a document in order to determine whether the document is genuine and a distributed computer system to carry out the method. The auditing is based on the analysis of areas of interest in an image of the document according to different categories, and score values for each of those categories. 
     US 2012/0059745 A1 pertains to a method for expense management. An expense data record is retrieved from a trusted source and two or more sub-transactions are identified. The expense data record is then added to an expense report as two or more expense items corresponding to the two or more sub-transactions. 
     SUMMARY 
     According to first aspect, a method of automatically auditing a document to determine whether the document is genuine is provided. The method comprises generating an image of the document to be audited, pre-processing of the image to obtain at least one segment of the image with an area of interest and dividing the at least one segment into portions containing single characters and/or combinations of characters. The method further comprises performing a validation of at least two single characters and/or at least two combinations of characters, the validation being carried out for each of the single character and/or character combinations for at least two different categories. The method further comprises creating score values for each category for each validated single character and/or character combination, creating feature vectors for each single character and/or character combination, wherein components of the feature vectors are the score values for the single character and/or character combination for each respective category. The method comprises classifying feature vectors to determine whether the single character or character combination to which the feature vector is associated is genuine. 
     According to a second aspect, a computer device to automatically audit a document to determine whether a document is genuine is provided. The computer device comprises at least one processor and at least one non-volatile memory comprising at least one computer program with executable instructions stored therein. The executable instructions, when executed by the at least one processor, cause the at least one processor to generate an image of the document to be audited, to pre-process the image to obtain at least one segment of the image with an area of interest, to divide the at least one segment into portions containing single characters and/or combinations of characters. The instructions further cause the processor to perform a validation of at least two single characters and/or at least two combinations of characters, the validation being carried out for each of the single character and/or character combinations for at least two different categories, as well as to create score values for each category for each validated single character and/or character combination and to create feature vectors for each single character and/or character combination, components of the feature vectors being the score values for the single character and/or character combination for each respective category. The instructions further cause the processor to classify the feature vectors to determine whether the single character or character combination to which the feature vector is associated is genuine. 
     According to a third aspect, a computer program product comprising program code instructions stored on a computer readable medium to execute the method steps according to the second aspect, when said program is executed on a computer, is provided. 
     According to the first aspect, a method of automatically auditing a document to determine whether the document is genuine is provided. The document may be a receipt, a diploma certificate, a doctoral certificate, or any other test certificate, a lease agreement, a hotel bill to be refunded or any other document of interest that might be subject to forgery. 
     The document may be tempered with in various ways, such as, inserting handwritten amendments, printing computer edited amendments on the document, manipulating the document using a graphics program, placing adhesive tapes/paper strips with altered content on an area of the document that should be overwritten, such as a pricing field, whipping out certain passages on the document and overwriting them etc. 
     The method comprises generating an image of the document to be audited. The image may be recorded by a camera of a dedicated scanning device, like an OCR (optical character recognition) scanner or the like. To provide another example, the image recorded by a camera of a multi-purpose mobile device, such as a mobile telephone, a tablet or the like. The device arranged to record the image of the document is, for example, equipped with at least one processor, at least one memory etc., as well, in order to further process the recorded image according to the activities to carry out the method explained in the following. 
     The dedicated device or application on a multi-purpose device may be, for example, implemented to provide a self-auditing method for e.g. employees of a company who want to check whether receipts they want to file for reimbursement of costs are valid. Further, also approval services for certificates may be implemented using such devices. 
     The method comprises pre-processing of the image to obtain at least one segment of the image with an area of interest and dividing the at least one segment into portions containing single characters and/or combinations of characters. 
     The area of interest is, for example, obtained using a YOLO (you only look once) neural network-based object detection algorithm. Areas that contain objects and are therefore non-empty are, for example, recognized as areas of interest by said YOLO based detection algorithm. The pre-processing of the image may further comprise dividing the segments of the image into at least two different sub-segments, wherein at least one of these sub-segments contains at least one character and at least one of these sub-segments does not contain any characters. The identification of characters is, for example, implemented by an OCR algorithm. 
     The method further comprises performing a validation of at least two single characters and/or at least two combinations of characters. The validation is carried out for each of the single character and/or character combinations for at least two different categories. The categories according to which the validation is performed, may have its foundation in a comparison of two single characters or corresponding groups of characters on the same document to each other. If a group of characters or single character differs from other, in some examples, adjacent (group of) character(s) more than a given threshold, this outlier, may have been subject to forgery. 
     The validation activities (including those described in the following) may be carried out on the basis of a recurrent convolutional neural network, which has been configured based on a training data set. 
     The method further comprises creating score values for each category for each validated single character and/or character combination. The score values may be determined by different methods applied to the single character and/or character combination for each category. Feature vectors are created for each single character and/or character combination, wherein components of the feature vectors are the score values for the single character and/or character combination for each respective category. The feature vectors may be used as input for a machine learning technique using an artificial neural network. The entirety of all created feature vectors, for example, forms a feature space for the characters located in the area of interest that are subject to the validation. 
     The method comprises classifying feature vectors to determine whether the single character or character combination, to which the feature vector is associated, is genuine. As each feature vector in this feature space represents a character or a group of characters, the classification of the feature vector allows conclusions about the properties of the underlying single character and/or group of characters. If, for example, these properties are too dissimilar to the properties of other single characters and/or groups of characters, for example, the deviating characters and/or groups of characters are finally deemed to be not genuine. Even if only a single character on the document to be audited appears not to be genuine, the entire document may be considered to be not genuine. 
     In some examples, the generation of said segments and/or the validation of the single characters and/or the character combinations and/or associated scoring of values for each category and/or the classification of the feature vectors is performed using an artificial neural network. In such a neural network, at least in one of the initial layers, each feature vector may be associated with a single neuron. The next layer of the neural network would, for example, be associated with functions performing a comparison of the feature vectors, in particular a cluster analysis as further explained below. This next layer could be coupled to the output of the neurons associated with respective feature vectors. 
     As mentioned above, a YOLO based convolutional artificial neural network may be used, for example, to first separate the empty segments from the non-empty segments of the document and then an OCR algorithm may be used to identify the single characters or group of characters that shall undergo the validation in these first identified non-empty regions. 
     Subsequently, the validation according to the categories to obtain the score values as components of the feature vectors may also involve applying an artificial neural network based algorithm. As such, for examples, differences with regard to font, background colour, or distance of characters are, for example, evaluated using artificial neural networks to obtain said score values for the categories font, background colour, or distance of characters. 
     In some examples, the categories used for the validation of the single character and/or character combinations includes at least one of font, overlay, background and foreground, font alignment, readability, completeness, usage of artificial filters, and steganographic manipulation. 
     Obtaining a score value for the font category may comprise an analysis of font deviations of characters under validation from characters adjacent to or in vicinity of the analysed characters. Obtaining a score value for the overlay category may comprise an analysis to whether digital or physical overlays have been used to cover certain characters on the document. For physical overlays, for example, a height difference between the character and the surrounding of the character may be evaluated. The readability, for example, may be evaluated on the basis of error values resulting from an OCR algorithm to obtain the respective score value. The usage of artificial filters may be detected, for example, by tracing artefacts of artificial filter use and consequently obtaining a score value. 
     In some examples, the validation of the single character and/or character combinations according to the background and foreground category comprises a bonding analysis of a character in a portion. The bonding analysis may comprise a comparison of the foreground and background of a character to be validated. For example, sharp transitions between foreground and background of a character are seen as an indication of manipulation and the score value is for the character in the category background and foreground is set accordingly. 
     In some examples, the validation of the single character and/or the character combination according to the font alignment category comprises obtaining the distance between two adjacent characters and/or combination of characters. In the font alignment category validation, for example, the distance between every other character in a group of character is determined. The score levels might be to a value indicating a low risk of manipulation if the distance between characters is uniformly distributed or even equal among the group of characters. However, for such characters that show a deviating distance to the other characters of the group, the score value may be set as to indicate a correspondingly higher risk of manipulation. 
     In some examples, the validation of the single character and/or the character combination according to the artificial filter category comprises passing each character through an analysis dedicated to the identification of manipulation caused by artificial filter use. The analysis might be performed based on determining noise distribution over the image, strong edges, lighting, or image metadata analysis. Based on this analysis a score value for the single character or the group of characters validated under the artificial filter category is set. 
     In some examples, the validation of the single character and/or the character combination according to the steganographic manipulation category comprises an error level analysis applied to the document, wherein the error level analysis comprises a comparison of the image with a compressed version of the image. 
     To detect certain type forgeries, like a user copying a certain portion of an image from another source, also steganographic features may be derived from the source image. An example for such a forgery would be a user who has tried to copy a date form another receipt and tried to replace it here. 
     When modifications are very subtle, using just the original image directly to derive final feature vectors of characters could be not sufficient in such cases due to a bad signal to noise ratio of the input. To overcome this, an error level of the image may be calculated, for example, by using a technique called error level analysis, and then the resultant image may be used to compute the feature vectors in addition to features derived from original image. An example for the error level of segments of a document is given by the following formula: 
     
       
         
           
             
               Error 
               ⁢ 
               
                   
               
               ⁢ 
               Level 
             
             = 
             
               
                 
                   Edited 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   image 
                 
                 - 
                 
                   Edited 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   image 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   at 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   95 
                   ⁢ 
                   % 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   compression 
                 
               
               Threshold 
             
           
         
       
     
     To obtain the error level, as in the formula, a compression of the image (e.g. resulting from a conversion to a jpeg file) may have to be calculated. Characters or groups of characters which have been manipulated by e.g. copying data from a different receipt of the same type may show a high error level so that the score value associated with this character or this group of characters may be set corresponding to a high probability of manipulation. 
     In some examples, the classification involves a cluster analysis, wherein a single-character cluster analysis is performed for each feature vector associated with a corresponding single character. In some examples, this single-character cluster analysis comprises obtaining a similarity indication between at least two feature vectors associated with single characters, wherein, when the similarity indication obtained violates a defined threshold, the single character associated with the corresponding dissimilar feature vector is considered to be non-genuine. Put differently, feature vectors that are comprised by a cluster defined by the similarity indication and the threshold are deemed to be indicative of genuine characters, whereas feature vectors that lie outside this cluster are deemed to be indicative of non-genuine characters. 
     This single-character cluster analysis is, for example, performed as a first tier in a multi-tier validation that ultimately involves three tiers. The second tier is, for example, the multi-character cluster analysis and the third tier may be the document-wide cluster analysis further described below. 
     Hence, in some examples, also a multi-character cluster analysis is performed for feature vectors associated with a plurality of characters, and a document-wide cluster analysis is performed for all feature vectors associated with the characters of the document. 
     Like the single-character analysis, the multi-character cluster analysis, for example, comprises obtaining a similarity indication between at least two feature vectors associated with a combination of characters. If the similarity indication obtained violates a defined threshold, then the plurality of characters associated with the corresponding dissimilar feature vector are considered to be non-genuine. Therefore, the similarity indication and the threshold together define—also for the multi-character cluster analysis—which feature vectors associated with a group of characters lie within or outside a cluster. Those groups of characters associated with feature values lying outside the cluster are deemed to be non-genuine. 
     In some examples, the document-wide cluster analysis comprises obtaining a similarity indication between a feature vector associated with a single character or a combination of characters, and an aggregated mean feature vector associated with all characters in the entire document. The aggregated mean feature vector associated with all characters in the entire document is, for example, calculated by aggregating the vector components for each single character and for each group of characters that have gone through a respective single character and multi-character analysis. Aggregating the vector components, for example, involves calculating a mean value of all score values for a particular category to obtain a mean-score value for this category. This mean value would then be the corresponding feature vector component of said feature vector associated with all characters in the entire document. Alternatively, the single-character and/or multi-character score values are, for example, aggregated by summing every up every feature vector component to obtain a respective component of the feature vector associated with all characters. 
     Like for the single-character cluster analysis and the multi-character cluster analysis, if the similarity indication obtained in the document-wide cluster analysis violates a defined threshold, the single character associated with the corresponding dissimilar feature vector is considered to be non-genuine. 
     Performing a validation not only on the single character level but in addition also on the level of character combinations and on a document wide level enhances the ability of the method to distinguish between attempts of forgery and minor degradations of receipt quality (dust, dirt, folded etc.). Further, also the precision of the method is enhanced, since forgery attempts like copying segments from similar receipts may not be detected on the single character level but only on the document-wide level by, for example, a comparison of the original document with a compression applied and the original document. 
     In some examples, obtaining the similarity indication comprises calculating a cosine similarity, wherein the calculation of the cosine similarity comprises calculating a dot product between at least two feature vectors and the magnitude of those at least two feature vectors. This technique may be implemented for calculating the similarity between feature vectors associated with single characters (single-character cluster analysis) and/or for calculating the similarity between feature vectors associated with a group of characters (multi-character cluster analysis), and/or for calculating the similarity between an aggregated mean feature vector associated with all characters and feature vectors associated with single characters and/or groups of characters. Alternatively, a different similarity indication may be used for calculating the similarities in the single-character cluster analysis and/or the multi-character cluster analysis and/or the document-wide cluster analysis. 
     In some examples, in particular such examples using a cosine similarity, the defined similarity threshold lies between 0 and 1 and the threshold is violated if the similarity indication is equal to or lower than said defined similarity threshold. 
     As such, for example, to find the similarity between two vectors A=[a 1 , a 2 , . . . , a n ] and 
     B=[b 1 , b 2 , . . . , b n ], the cosine similarity of these two vectors A and B (or more precisely the cosine of the angle between the two vectors A and B, which represents the similarity score) is calculated using the following formula: 
     
       
         
           
             
               
                 similarity 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 score 
               
               = 
               
                 
                   A 
                   . 
                   B 
                 
                 
                   
                      
                     A 
                      
                   
                   ⁢ 
                   
                      
                     B 
                      
                   
                 
               
             
             , 
           
         
       
     
     wherein ∥A∥ ∥B∥, corresponds to the (euclidean) l 2  norm of the vectors A and B. The calculation of this norm involves the calculation of the dot product A*A and B*B. 
     The similarity score lies between 0 and 1. A value close to 0 means dissimilar, close to 1 means similar. A threshold t may be defined, where 0≤t≤1. 
     The similarity y may be defined by: where 
     
       
         
           
             y 
             = 
             
               { 
               
                 
                   
                     
                       
                         
                           similar 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           if 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           s 
                         
                         &gt; 
                         t 
                       
                     
                   
                   
                     
                       
                         
                           dissimilar 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           if 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           s 
                         
                         ≤ 
                         t 
                       
                     
                   
                 
                 . 
               
             
           
         
       
     
     According to the second aspect, a computer device to automatically audit a document to determine whether a document is genuine is provided. The computer device comprises at least one processor and at least one non-volatile memory comprising at least one computer program with executable instructions stored therein. The executable instructions, when executed by the at least one processor, cause the at least one processor to generate an image of the document to be audited, to pre-process the image to obtain at least one segment of the image with an area of interest, to divide the at least one segment into portions containing single characters and/or combinations of characters. The instructions further cause the processor to perform a validation of at least two single characters and/or at least two combinations of characters, the validation being carried out for each of the single character and/or character combinations for at least two different categories, as well as to create score values for each category for each validated single character and/or character combination and to create feature vectors for each single character and/or character combination, components of the feature vectors being the score values for the single character and/or character combination for each respective category. The instructions further cause the processor to classify the feature vectors to determine whether the single character or character combination to which the feature vector is associated is genuine. 
     According to the third aspect, a computer program product comprising program code instructions stored on a computer readable medium to execute the method steps according to the second aspect, when said program is executed on a computer, is provided. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Examples of the invention are now described, also with reference to the accompanying drawings. 
         FIG. 1  shows a mobile device scanning a receipt in order to carry out the method further described in  FIGS. 2 to 19 . 
         FIG. 2  is a flow chart illustrating activities of the method of automatically auditing a document to determine whether the document is genuine. 
         FIG. 3  illustrates different examples for an auditing result. 
         FIG. 4  shows examples of receipt features that are analysed and possible audit recommendations following from the analysis. 
         FIG. 5  shows three different examples of manipulated and/or altered receipts. 
         FIG. 6  shows three different examples of degraded receipts. 
         FIG. 7  shows different validation categories for a pricing field of a receipt. 
         FIG. 8  schematically illustrates an example for identifying areas of interest on a receipt. 
         FIG. 9  is a schematic block diagram of an example for a process flow from text localization to the presentation of audit results. 
         FIG. 10  shows examples of score values for different areas of on the receipt. 
         FIG. 11  shows a table illustrating examples for different score values for different single characters and character combinations for different categories. 
         FIG. 12  shows an example for a validation of single characters in the pricing field in the background and foreground (bonding) category. 
         FIG. 13  shows an example for a validation of single characters in the pricing field in the artificial filter analysis category. 
         FIG. 14  shows an example for a validation of single characters in the pricing field in the character distance category. 
         FIG. 15  shows examples for feature vectors associated with single characters formed based on a manipulated character combination in component representation as well as in their alignment in a feature space and groups. along with clusters encompassing some of these vectors. 
         FIG. 16  shows examples for feature vectors associated with a character combination formed based on a manipulated character combination in component representation as well as in their alignment in a feature space along with clusters encompassing some of these vectors. 
         FIG. 17  shows examples for feature vectors associated with characters combinations as well as an aggregated mean feature vector for the entire document in component representation as well as their alignment in a feature space along with clusters encompassing some of these vectors. 
         FIG. 18  illustrates a schematic flow diagram of an example for a method of calculating a similarity indication of feature vectors. 
         FIG. 19  shows a schematic computerized system on which the methods illustrated by  FIGS. 2 to 18  could be implemented. 
     
    
    
     The drawings and the description of the drawings are of examples of the invention and are not of the invention itself. Like reference signs refer to like elements throughout the following description of examples. 
     DETAILED DESCRIPTION 
     An example of a mobile device  1 , in this example a mobile phone, which is scanning a receipt  2  is illustrated by  FIG. 1 . An image may be generated by a camera of the mobile device  1  (not shown) that is then further analysed to audit whether and to which extent the receipt  2  is genuine. The audit system may be a self-audit system, with which an employee could check whether the receipt presented is likely to be accepted by, e.g. an expense reimbursement department of his or her company. 
     A flow chart illustrating activities of the method of automatically auditing a document to determine whether the document is genuine, is illustrated by  FIG. 2 . 
     In an activity  200  an image of the document to be audited is generated. In subsequent activity  201 , the image is pre-processed to obtain at least one segment of the image with an area of interest. In a subsequent activity  202 , the at least one segment is divided into portions containing single characters and/or combinations of characters. In a subsequent activity  203  a validation of at least two single characters and/or at least two combinations of characters is performed. The validation is carried out for each of the single character and/or character combinations for at least two different categories. In a next activity  204 , score values are created for each category for each validated single character and/or character combination. In a subsequent activity  205 , feature vectors for each single character and/or character combination are created. The components of these feature vectors are score values for the single character and/or character combination for the respective category. In a subsequent activity  206 , the feature vectors are classified to determine whether the single character or character combination to which the feature vector is associated is genuine. 
     Two different examples for an auditing result are illustrated by  FIG. 3 . The audit result  3  (check sign), which is for example shown on a screen of a mobile device  1  (see  FIG. 1 ) indicates that the receipt is genuine and that, for the use case of expense reimbursement, the receipt can be used for said expense reimbursement. The x-sign related to a self-audit result  3 ′ may indicate that the receipt is not accepted, e.g. by an expense reimbursement system. In the specific example illustrated in  FIG. 3 , the receipt  20  is not accepted since it is not readable. Other grounds for rejection that are also illustrated in  FIG. 3  are, for example, faded or blurred text, the vendor or the amount being not visible. The suggestion that may be displayed on a screen of a mobile device  1  is to provide proper receipts. 
     Examples of receipt features that are analysed and possible audit recommendations following from the analysis are illustrated by  FIG. 4 . In the analysis stage  4 , the following activities may be performed on the receipt  2 : analysing the type, the content type, the relevance or the scope of the receipt. Furthermore, an analysis of potential unknown features or content problems may be performed. The analysis stage is, for example, followed by the following results  5 . A possible result of the analysis of the type of the receipt, would be a taxi, a restaurant, a pub, a bus, a metro, tram, train or flight. The result for the content type of the receipt may be food, travel, training, a conference or the lie. The result of the relevant might yield out of policy, in policy or a degree of compliance with policy in percent. The analysis of the scope might result in approving the scope of improvements (e.g. a class upgrade in a flight) as being valid or invalid. The analysis of unknown feature might yield the result that the unknown feature is a stamp, a handwritten text or that patches are present. The analysis of the content problems may provide the result that the receipt is folded, not readable, torn, a duplicate or that the text on the receipt is very small (maybe too small). These results  5  of the analysis  4  might lead to audit recommendations  6 . These audit recommendations  6  may be at least one of the following: Produce a proposer receipt, the receipt is out of policy, the receipt is tampered, the receipt has handwritten text on it, or others. 
     Three different examples of manipulated and/or altered receipts  21 ,  22 ,  23  are shown in  FIG. 5 . As can be taken from said figure, the area of interest  71  of receipt  21  has been tampered by a stamp over the amount section  8 . The area of interest  72  of receipt  22  has been tampered in that the amount has an overlay  81 . The area of interest  73  of the receipt  23  has been tampered by overlaying patches  82 . In all three cases, the area of interest  71 ,  72 ,  73  is an amount field  11  (see also the  FIGS. 7 to 8 and 10 to 14 ). 
     Three examples of degraded receipts are shown in  FIG. 6 . The receipt  20  shown there is deemed not readable  20 , whereas the receipt  24  is a torn receipt  84  and the receipt  25  is crumbled. 
     A variety of categories, dependent on which single characters and/or combinations of characters from an amount field  11  of a receipt  2  are validated are shown in  FIG. 7 . Those categories are font family and size  10 , overlay element  101 , neighbourhood background and foreground features  102 , font alignment, rotation and perspective  103 , human/OCR readability  104 , completeness  105 , and the presence of manipulation by artificial filters  106 . 
     An example for identifying areas of interest on a receipt is illustrated by  FIG. 8 . The recognition of areas of interest  11  on the receipt  26  is carried out here by identifying areas of interest using a YOLO (you only look once) algorithm  201 ′. The YOLO algorithm may be part of a multilayer artificial neural network that is used to perform the audition to check whether the receipt is genuine. 
     A schematic block diagram of an example for a process flow from text localization to the presentation of audit results is provided by  FIG. 9 . A text localization algorithm (for example one as the YOLO algorithm, see  FIG. 8 ) is applied to the receipt  26 . The segment containing text is analysed using a recurrent convolutional neural network  32 . 
     This analysis results in a variety of score values for each category: In the font category  10 , there is a score value  40  with the assumed value 0. For the overlay category  101 , the corresponding score value  50  assumes the value 0. In the background (colour) vs foreground (colour) category the corresponding score value  60  also assumes the value 0. The validation in the OCR readability category  104  results in a corresponding score value  70  with a value of 1. A corresponding score  75  in the completeness category  105  has a value of 1. Finally, the validation in the artificial filter category  106  yields a corresponding score value  80  with a value of 0. The validation and scoring performed in this first step may be a single character validation. The overall result is a feature vector  160  with the respective score values  40 ,  50 ,  60 ,  65 ,  70 ,  75 ,  80  for each category as its components. The feature vector of this example has the following shape in component representation: (0,0,0,0,1,1,0). 
     Either based on the scores for each character, a word aggregate scores (mean)  33  are calculated in the example illustrated by  FIG. 9 . The result for these aggregate scores is in this example: Overlay=0.1; OCR readability=0.4; Completeness=0.2, text alteration=0.1 etc. The word aggregate scores (mean) could be calculated based on previously calculated single character scores. However, they could also be calculated from scratch by passing character combinations recognized as words through a validation according to the above-mentioned categories. 
     In the example of  FIG. 9 , also document aggregate score (mean) are, for example, calculated, for example, based on scores for single characters or on the word aggregate (mean) scores. Also here, the score could alternatively be calculated from scratch. In the example illustrated by  FIG. 9 , the document aggregate (mean) score values are as follows: Overlay=0.03; OCR readable=0.92; Completeness=0.90; text alteration=0.02. 
     An example for a concrete scoring of concrete passages on the receipt  2  is provided by  FIG. 10 . The amount field  11  yields a score  13  of 99%. The text “Einlösen des Pfandbonds nur in dieser Filiale!” yields an overall score  13   a  of 89%. The bar code gets a score value  13   b  of 59%. The serial number and date field, when validated, receives a score value  13   c  of 59%. 
     A plurality of feature vectors resulting from a single character and a combination of character analysis of the amount field  11  with the entry “€ 0.25” is schematically illustrated in  FIG. 11 . The five single characters “€”, “0”, “.”, “2”, “5”  14  are validated according to the same categories as in the example of  FIG. 9  to obtain five corresponding feature vectors (see  FIGS. 9, 15 to 17 ). The entry “€ 0.25” is further subdivided into the character combinations “€ 0”, “0.”, “0.2”, “25”, “€ 0.”, “0.2”, “0.25”, “€ 0.2”, “0.25”  15 . The values from the resulting feature vectors can be taken from the table  1600  shown in  FIG. 11 . 
     An example for a validation of single characters in the pricing field  11  in the background and foreground (bonding) category  102  (see  FIGS. 9 and 11 ) is shown in  FIG. 12 . The single characters “€”, “0”, “.”, “2” and “5” are extracted from the amount field  11  using, for example, by applying a YOLO algorithm and an OCR algorithm. These single characters then undergo a validation using a background and foreground analysis algorithm that is for example based on an artificial neural network. The bonding analysis, for example, is performed by comparing the area immediately surrounding a character and the character itself (foreground) with the background the character in which the character is embedded. For example, if there is a sudden transition in colour and/or texture, the outcome of the validation regarding this character would be a score value indicative of an anomaly (possibly a forgery). 
     The background and foreground analysis/prediction  120  as shown in  FIG. 12  is illustrated there once for an authentic version of the character “2” and once for a tampered version of the character “2”  16 . In the tampered version of “2”  16  a pad with the figure “3” has been put over the actual figure “2”. As can be seen in  FIG. 12 , the score value  60  resulting from the validation of the character “€” is 0.8, the score value  61  for the character “0” is 0.7, the score value  62  for the character “.” is 0.8, the score value  63  for the untampered character “2” is 0.7, while the score value  64  for the tampered character is 0.1. The score value  65  results from the character “5”. The score value  63  for the untampered character “2” 0.7 significantly differs from the score value for the tampered character  16  being 0.1. Further, as can be taken by comparing the score values  81 ,  81 ,  82 ,  83  and  85 . 
     An example for a validation of single characters in the pricing field  11  in the artificial filter category  106  (see  FIGS. 9 and 11 ) is shown in  FIG. 13 . The same validation principle as  FIG. 12  may be applied here (artificial neural network-based validation), with also the artificial filter validation  130  providing a significantly deviating score value for the tampered character “3”. 
     An example for a validation of single characters in the pricing field  11  in the character distance category  140  is shown in  FIG. 14 . The same validation principle as  FIGS. 12 and 13  may be applied here (artificial neural network-based validation), with also the character distance validation  140  providing a significantly deviating score value for the tampered character “3”. In this validation a score value  90 ,  91 ,  92 ,  93 ,  94 ,  95  is dedicated to the distance of a character to adjacent characters, so that deviating distances, indicative of manipulation, can be identified. 
     A set of feature vectors  150  to  155  obtained from a pricing field  11 ′ are shown in  FIG. 15 . The feature vectors  150  to  155  depicted there are presented in component representation as well as embedded in a coordinate system to schematically show their alignment in an n-dimensional feature space. 
     The first feature vector  150  is associated with the entire character combination “€ 0.35”, including a manipulated character “3”, that should actually read “2” (see  FIGS. 12 to 14 ). The score values for the corresponding categories (see  FIGS. 9 to 14 ) for the combination of characters “€ 0.35” are the components of the feature vector  150 . The feature vectors associated with the single characters “€”, “0”, “.”, “3” and “5”  150  to  155  also have score values resulting from a validation according to the above-mentioned categories as their components. The feature vector  151  is associated with the character “€”, the feature vector  152  is associated with the character “0”, the feature vector  153  is associated with the character “.”, the feature vector  154  is associated with the manipulated character “3” and the feature vector  155  is associated with the character “5”. 
     The alignment of the feature vectors  151 ,  152 , 153 , associated with single characters “€”, “0”, “.” respectively, and of feature vector  150  associated with the untampered character combination “€ 0.25” is illustrated in a feature space (x,y) coordinate system in the lower left corner of  FIG. 15 . As can be seen there, the feature vectors associated with genuine characters  151  to  153  and  156  are aligned within a cluster  168 , in which also the hypothetical feature vector of a genuine character combination “€ 0.25”  11  lies. The feature vector associated with the tampered character “3”  164 , however, lies outside this cluster  168 . 
     As depicted in the lower right corner of  FIG. 15 , an outside cluster  169  can be defined, wherein each feature vector associated with a character or character combination lying inside this outside cluster  169  is defined to be indicative of a manipulation since the angle between a feature vector associated with the genuine character combination  11  is bigger than a threshold. The feature vector  154  associated with the tampered character “3”  164  lies inside this cluster  169 . 
     A component representation of feature vectors  170  to  174  associated with combinations of characters  11 ″,  181  to  184 , and the schematic alignment of these vectors in an n-dimensional feature space is illustrated by  FIG. 16 . 
     The feature vector is 170 is associated with the character combination “€ 0.39”  11 ″, of which the single characters “3” and “9” are manipulated. The feature vector  171  is associated with the character combination “€ 0”  181 , the feature vector  172  is associated with the character combination “0.”  182 , the feature vector  173  is associated with the partly tampered character combination “0.3”  183  and the feature vector  174  is associated with the entirely tampered character combination “39”  184 . 
     Like in  FIG. 15 , the alignment of certain feature vectors and a cluster of certain feature vectors is shown in the lower left corner of  FIG. 16 . The feature vectors  171  to  174  and the feature vector  175  (see  FIG. 15 ) are aligned within a cluster  188  in the n-dimensional feature space depicted there. The only feature vector that is outside the cluster  188  is the feature vector  174  associated with the entirely tampered character combination “39”  184 . 
     As can be seen in the feature space representation shown in the lower right corner of  FIG. 16 , an outside cluster  189  could be defined around the feature vector  174  associated with character combination “39”  184 . Note that dependent on the defined size of the outside cluster  189 , the character combination “39”  184  may be recognized as manipulated since it lies within said cluster. 
     Examples for feature vectors associated with characters combinations  191 ,  192 ,  193  as well as an aggregated mean feature vector associated with all characters in the entire document  190  are depicted in  FIG. 17 . As for  FIGS. 15 and 16 , these feature vectors  190 ,  191 ,  192 ,  193 , are depicted in component representation as well as their alignment in a feature space along with clusters encompassing some of these vectors. 
     As mentioned above, creating the aggregated mean feature vector, for example, involves calculating a mean value of all score values for a particular category to obtain a mean-score value for this category. This mean value would then be the corresponding feature vector component of said feature vector associated with all characters in the entire document. Alternatively, the single-character and/or multi-character score values are, for example, aggregated by summing every up every feature vector component to obtain a respective component of the feature vector associated with all characters. 
     As in  FIG. 16 , the example presented in  FIG. 17  relates to the manipulated character combination “€ 0.39”  170  (see  FIG. 16 ), wherein the characters “25” have been replaced by the characters “39”. 
     In the example illustrated by  FIG. 17 , the aggregated mean feature vector  190  is associated with an aggregated score for the entire document. The feature vector  191  is associated with the tampered character combination “€ 0.39” whilst the feature vector  192  is associated with the character combination “0.3” and the feature vector  193  is associated with the character combination “39”. 
     A cluster  198  is defined that lies outside the aggregated mean feature vector associated with all characters in the entire document  190 . It can be seen in the feature space representation in the lower left corner of  FIG. 17 , that the feature vectors associated with the character combinations “0.3” and “39” lie outside this cluster  198 , whilst the hypothetical vector associated with the genuine character combination “€ 0.25” lies within said cluster  198 . 
     As can be seen in the depiction in the lower right-hand side of  FIG. 17 , an outside cluster  199  can be defined that includes both feature vectors  192  and  193  associated with the character combinations “0.3” and “39”, respectively. 
     Whether or not a feature vector lies within a cluster  168 ,  188 ,  198 ,  169 ,  189 ,  199  may depend on the similarity between two feature vectors. 
     A schematic flow diagram of an example for a method of calculating such a similarity indication of feature vectors is shown in  FIG. 18 . 
     In an activity  400  to find the similarity y between two vectors A=[a 1 , a 2 , . . . , a n ] and B=[b 1 , b 2 , . . . , b n ], the cosine similarity of these two vectors A and B (or more precisely the cosine of the angle between the two vectors A and B, which represents the similarity score) is calculated using the following formula: 
     
       
         
           
             
               
                 similarity 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 score 
               
               = 
               
                 
                   A 
                   . 
                   B 
                 
                 
                   
                      
                     A 
                      
                   
                   ⁢ 
                   
                      
                     B 
                      
                   
                 
               
             
             , 
           
         
       
     
     wherein ∥A∥ ∥B∥, corresponds to the (euclidean) l 2  norm of the vectors A and B and the similarity score s lies between 0 and 1. The calculation of this norm involves the calculation of the dot product A*A and B*B. 
     Subsequently, a threshold t is defined in an activity  410 , wherein the threshold lies between 0 and 1. Thereafter in a comparison activity  420  it is checked whether the similarity score s is equal or smaller than the defined threshold t. 
     In response to the comparison activity  420  yielding the result that the similarity score s is not smaller or equal to the threshold t the feature vectors A and B are considered to be dissimilar in activity  440 . In response to the comparison activity  420  resulting in the finding that the similarity score s is indeed smaller or equal to the threshold t, the feature vectors A and B are considered to be similar in activity  430 . 
     By choosing the threshold value t accordingly, a more or less restrictive similarity criterion can be set. As mentioned above, the similarity score s, corresponding to the cosine of the angle between two feature vectors may define the size of a cluster just as those described in conjunction with  FIGS. 15 to 17 . 
     A diagrammatic representation of an exemplary computer system  500  is shown in  FIG. 19 . The processor  502  is arranged to execute a set of instructions  503 , to cause the computer system  500  to perform any of the methodologies used for the method of automatically auditing a document to determine whether the document is genuine, as described herein. The mobile device  1  (see  FIG. 1 ) might be arranged like this. 
     The computer system  500  includes a processor  502 , a main memory  504  and a network interface  508 . The main memory  504  includes a user space, which is associated with user-run applications, and a kernel space, which is reserved for operating-system- and hardware-associated applications. The computer system  500  further includes a static memory  506 , e.g. non-removable flash and/or solid state drive and/or a removable Micro or Mini SD card, which permanently stores software enabling the computer system  500  to execute functions of the computer system  500 . Furthermore, it may include a video display  510 , a user interface control module  514  and/or an alpha-numeric and cursor input device  512 . Optionally, additional I/O interfaces  516 , such as card reader and USB interfaces may be present. The computer system components  502  to  516  are interconnected by a data bus  518 . 
     In some exemplary embodiments the software programmed to carry out the method described herein is stored on the static memory  506 ; in other exemplary embodiments external databases are used. 
     An executable set of instructions (i.e. software)  503  embodying any one, or all, of the methodologies described above, resides completely, or at least partially, permanently in the non-volatile memory  506 . When being executed, process data resides in the main memory  504  and/or the processor  502 .