Method and Device for Fitness Testing of Value Documents

Abstract

The invention relates to the fitness check of value documents. For at least two fitness criteria there is respectively determined with the aid of an unfit function an unfit degree of the particular value document. The unfit function clearly assigns an unfit degree to the fitness measurement values and has two threshold values, beyond which the unfit degree with respect to the relevant fitness criterion is 0 or 1. Between the threshold values there is an uncertainty range in which the unfit degree is, with respect to the relevant fitness criterion, between 0 and 1 and the unfit function behaves monotonously dropping or monotonously rising. Subsequently, the unfit degrees of different fitness criteria are combined into an unfit probability of the particular value document and on the basis of the unfit probability a fitness classification of the particular value document is carried out.

Claims

1-15. (canceled)

16. A method for checking the fitness of value documents (A, B, C), comprising the following steps: selecting at least two different fitness criteria (K1, K2) of the value documents which are characteristic for the state of the value documents, checking the value documents by picking up measurement data of the value documents, wherein, from the measurement data picked up for each of the selected fitness criteria, a fitness measurement value (M1, M2) for the particular value document is determined, determining respectively an unfit degree (G1, G2) for each of the selected fitness criteria (K1, K2) from the particular fitness measurement value of the particular value document (A, B, C) with the aid of an unfit function (F1, F2) which clearly assigns an unfit degree (G1, G2) to each fitness measurement value (M1, M2), wherein each unfit function is characterized by a first threshold value (X1, X2), a second threshold value (Y1, Y2), and an uncertainty range (U1, U2) being between the first and the second threshold value in which the particular unfit function either has a monotonously dropping or a monotonously rising course, and combining the unfit degrees (G1, G2) of the different fitness criteria (K1, K2) into an unfit probability (P) which is specific for the particular value document (A, B, C), and fitness classification of the particular value document on the basis of the unfit probability (P) which was determined for the particular value document.

17. The method according to claim 16, wherein the unfit function (F1, F2) assigns to the fitness measurement values (M1, M2) being in the uncertainty range respectively one unfit degree (G1, G2) which is greater than 0 and lower than 1 and between the first and second threshold value either assumes a monotonously dropping or monotonously rising, e.g. linear or nonlinear, course, wherein the unfit function assigns in particular an unfit degree of 0 to all fitness measurement values being beyond the first threshold value and assigns an unfit degree of 1 to all fitness measurement values being beyond the second threshold value.

18. The method according to claim 16, wherein the selected fitness criteria (K1, K2) relate to one or several of the following properties of the value documents: soiling, wear, damage, alien elements or limpness of the particular value document, wherein the selected fitness criteria preferably relate to at least two different ones of these properties.

19. The method according to claim 16, wherein at least two such fitness criteria are selected in which the frequency distribution of the fit value documents and the frequency distribution of the unfit value documents overlap each other as little as possible, wherein the two frequency distributions preferably have a maximum overlap of 30%.

20. The method according to claim 16, wherein upon combining the unfit degrees (G1, G2, . . . ) of the different fitness criteria (K1, K2) into the unfit probability (P) there is carried out a multiplication of the unfit degrees of the different fitness criteria, in particular that the unfit probability (P) is determined from the unfit degrees (G2, G2) according to the following formula: $P=1-\underset{j}{.Math.}.Math.{\left(1-G_j\right)}^{k_j}=1-{\left(1-G.Math..Math.1\right)}^{k_1}.Math.{\left(1-G.Math..Math.2\right)}^{k_2}.Math.\mathrm{.Math.}$

21. The method according to claim 16, wherein upon combining the unfit degrees (G1, G2, . . . ) of the different fitness criteria into the unfit probability (P) there is formed a linear combination of the unfit degrees of the different fitness criteria, in particular by adding up the unfit degrees (G1, G2, . . . ) of the different fitness criteria, where applicable with different weighting of the unfit degrees.

22. The method according to claim 16, wherein, for the fitness classification of the particular value document, the unfit probability (P) determined for the value document is compared with a fitness threshold (T) and the value document is classified as unfit, if the unfit probability (P) exceeds the fitness thresholds (T).

23. The method according to claim 16, wherein upon the fitness classification of the value documents of a value document group to be checked for fitness there is carried out an advance calculation, in which, for different values of the fitness thresholds (T), respectively the expected unfit portion of the particular value document group is determined, which indicates the portion of value documents which are classified as unfit in the fitness classification of the particular value document group, and that information is generated about how the unfit portion depends on the value of the fitness threshold (T), wherein this information in particular is communicated to the user of a value-document processing apparatus carrying out the method for checking the fitness, e.g. by outputting it at a user interface of the value-document processing apparatus.

24. The method according to claim 16, wherein prior to the fitness check the following steps are carried out: providing a first group of fit value documents and a second group of unfit value documents, wherein the categorization of the value documents as fit or unfit was carried out in particular by a manual check by a person or by checking the value documents by means of a reference measuring system, checking the fit and the unfit value documents of the first and second group by picking up measurement data of these value documents with the aid of a measuring device, determining at least one fitness measurement value (M1, M2) for each of the value documents from the measurement data of the particular value document, determining a first frequency distribution of the particular fitness measurement value for the first group of the fit value documents and a second frequency distribution of the particular fitness measurement value for the second group of the unfit value documents, employing the first and second frequency distribution of the particular fitness measurement value (M1, M2) to select the fitness criteria (K1, K2) to be employed in the fitness check of the value documents and/or to determine the unfit function (U1, U2) of the particular fitness criterion (K1, K2).

25. The method according to claim 16, wherein for the value documents of at least one value document group to be checked for fitness the following steps are carried out after the fitness classification of the value documents of the value document group: ascertaining the unfit portion of the value document group, which indicates the portion of value documents which are classified as unfit in the fitness classification of the value document group, checking the unfit portion for at least one specification determined for the unfit portion, changing the unfit function (U1, U2) of one or several of the employed fitness criteria (K1, K2) in dependence on the ascertained unfit portion of the value document group, wherein, if the ascertained unfit portion fulfills the determined specification, the unfit function is left unchanged, and if the unfit portion does not fulfil the determined specification, the unfit function is changed and the following steps a)-f) are carried out, within the framework of a simulation, using the changed unfit function: a) determining anew the unfit degrees (G1, G2) of the particular value document for the at least two different fitness criteria (K1, K2) from the particular fitness measurement value using the changed unfit function of the particular fitness criterion, b) combining anew the unfit degrees of the different fitness criteria into an unfit probability (P) of the particular value document and c) classifying anew the fitness of the particular value document with the help of the particular unfit probability (P), d) ascertaining anew the unfit portion of the one or several value document groups, which indicates the portion of value documents which are classified as unfit in the fitness classification of the particular value document group, e) checking anew the unfit portion for the determined specification, f) changing anew the unfit function of one or several of the employed fitness criteria in dependence on the ascertained unfit portion of the value document group, wherein, if the ascertained unfit portion fulfills the determined specification, the unfit function is left unchanged, and if the ascertained unfit portion does not fulfil the determined specification, the unfit function is changed and the steps a)-f) are repeated within the framework of the simulation.

26. The method according to claim 25, wherein as soon as the unfit portion fulfills the determined specification, the unfit function is left unchanged and the fitness classification (step c) last carried out is employed as the final fitness classification and/or that then the unfit function last employed (for the fitness classification in step c) is employed for the future fitness classification of further value document groups, in particular for further value document groups of the same value document type.

27. The method according to claim 25, wherein upon changing the unfit function (U1, U2) of the particular fitness criterion (K1, K2), the unfit function of this fitness criterion is changed in dependence on the unfit portion of the value document group such that the unfit portion upon the new fitness classification is changed in comparison to the unfit portion ascertained before, for example is increased or is decreased, wherein the steps a)-f) according to claim 10 in particular are repeated so often, until the newly ascertained unfit portion corresponds at least approximately to that unfit portion which was detected before in a manual fitness check or in an automatic fitness check with the aid of a value-document processing apparatus for this value document group.

28. The method according to claim 16, wherein at least one of the fitness measurement values (M1, M2) is an aggregated fitness measurement value (MK), in which there are aggregated at least two different fitness measurement values (M3, M4), e.g. by linear combination of these fitness measurement values (M1, M2), and that at least one of the unfit degrees (G1, G2) is a group unfit degree (G) which indicates the fitness of the particular value document with respect to at least two different fitness criteria, wherein the group unfit degree (G) is determined with the aid of an unfit function (F) which was formulated for the aggregated fitness measurement value (MK).

29. The method according to claim 28, wherein for the value documents there is respectively determined a first group unfit degree (G) for a first group of at least two fitness criteria which respectively relate to the soiling of the particular value document, and that there is determined a second group unfit degree for a second group of at least two fitness criteria which respectively relate to the damage of the particular value document, wherein the unfit probability (P) of the particular value document in particular is determined by combining the first group unfit degree (G) relating to the damage with the second group unfit degree relating to the soiling of the bank note, and where applicable with one or several further unfit degrees and/or group unfit degrees.

30. An apparatus for checking the fitness of value documents (A, B, C) by the method according to claim 16, comprising: a measuring device for picking up measurement data of the value documents, and an evaluation device for the fitness classification of the value documents on the basis of the measurement data picked up, wherein the evaluation device is configured for selecting at least two different fitness criteria (K1, K2) of the value documents, which are characteristic for the state of the value documents, determining, from the measurement data picked up for each of the selected fitness criteria, a fitness measurement value (M1, M2) for the particular value document, determining, for each of the selected fitness criteria, respectively one unfit degree (G1, G2) from the particular fitness measurement value of the particular value document with the aid of an unfit function (F1, F2), wherein, the unfit function (F1, F2) clearly assigns an unfit degree (G1, G2) to each fitness measurement value (M1, M2), and wherein each unfit function is characterized by a first threshold value (X1, X2), a second threshold value (Y1, Y2), and an uncertainty range (U1, U2) being between the first and the second threshold value in which the particular unfit function either has a monotonously dropping or a monotonously rising course, and combining the unfit degrees (G1, G2) of the different fitness criteria (K1, K2) of the particular value document into an unfit probability (P) which is specific for the particular value document (A, B, C), and carrying out a fitness classification of the particular value document (A, B, C) on the basis of the unfit probability (P) which was determined for the particular value document.

Description

[0083] Further advantages of the present invention are to be found in the dependent claims and in the following description of the embodiment examples. There are shown:

[0084] FIG. 1a frequency distribution of the fitness measurement value M1 for value documents which are fit for circulation (fit) and non-fit for circulation (unfit),

[0085] FIG. 1b hitherto usual fitness classification by means of a threshold,

[0086] FIG. 2a frequency distribution of the fitness measurement value M1 of a fitness criterion K1 for fit and for unfit value documents,

[0087] FIG. 2b-c two examples of an unfit function for fitness criterion K1,

[0088] FIG. 3a frequency distribution of the fitness measurement value M2 of a fitness criterion K2 for fit and for unfit value documents,

[0089] FIG. 3b example of an unfit function for the fitness criterion K2,

[0090] FIG. 4 basic structure of a bank note processing machine,

[0091] FIG. 5a-c unfit functions for three different fitness criteria,

[0092] FIG. 6a-b table for the fitness evaluation (FIG. 6a) with the help of three different fitness criteria and unfit probability (FIG. 6b) ascertained therefrom for three value documents A, B, C

[0093] FIG. 7a-b aggregating of fitness measurement values and group unfit degree for the aggregated fitness measurement value.

[0094] In FIG. 4 there is represented a bank note processing machine 1 having an input pocket 20 into which bank notes 10 to be processed can be inserted, e.g. bank notes that are to be separated into bank notes fit for circulation (fit) and those unfit for circulation (unfit). The bank notes 10 are transferred by a singler 25 singly, one after the other, to a transport system 30. The transport system 30 transports the single bank notes through the bank note processing machine, past a measuring device 41 into one or several output pockets 32, 34. In doing so, the bank notes of different fitness can be sorted into different output pockets.

[0095] The measuring device 41 includes one or several sensors whose measurement data allow inferences about the state of the particular bank note, so that the bank note can be evaluated and categorized as fit or unfit. The sensors of the measuring device 41 may be for example one or several optical sensors having suitable light sources, the sensors detecting light reflected by the particular bank note or transmitted through the particular bank note, e.g. light of a certain wavelength or a certain wavelength range. Further sensors can check for example acoustic (e.g. ultrasound) and/or mechanical (e.g. thickness measurement) and/or thermal and/or magnetic and/or electrical properties of the particular bank note. The measurement data of the stated sensors permit statements on whether the particular bank note is soiled or damaged or whether it has alien elements such as clips or adhesive tape which affect the fitness for circulation of the particular bank note.

[0096] On the basis of the measurement data provided by the measuring device 41, an evaluation device 40 determines the fitness of the particular bank note, e.g. whether the particular bank note is a fit or an unfit bank note. The evaluation device 40 has e.g. a microprocessor which executes software for the fitness check which is stored in an associated memory. In dependence on the fitness of the bank note detected by the evaluation device 40, gates 31 and 33 are driven in the transport system 30 to place for example fit bank notes in a first output pocket 32 and unfit bank notes in a second output pocket 34. Further gates or output pockets may be provided in the transport system 30 of the bank note processing machine 1 and are indicated by a continuation 35.

[0097] A user interface 45 connected to the evaluation device 40 and consisting e.g. of a keyboard and a display or a touchscreen is employed for operating the bank note processing machine 1 by an operator. Via the user interface 45 commands can be entered or processing modes can be selected and processing results can be displayed or the user can be prompted by means of instructions to perform certain actions. The user interface can be accessed directly or also by means of remote control.

[0098] For checking the fitness of bank notes with respect to a certain fitness criterion, hitherto usually a fitness measurement value M of the bank note is compared with one single threshold value X. This threshold value is selected such that it is at a fitness measurement value between the frequency distribution for fit bank notes and the frequency distribution for unfit bank notes, cf. FIG. 1a. If the fitness measurement value of the bank note is above the particular threshold value X, the particular bank note is classified as unfit, otherwise as fit, cf. FIG. 1b. Hitherto, for each fitness criterion there is carried out such a comparison, and if one (or several) of the fitness measurement values M of the bank note exceeds its particular threshold value X, the particular bank note is categorized as unfit.

[0099] In FIG. 2a there are shown the same two frequency distributions for a fitness measurement value M1 of a fitness criterion K1 as in FIG. 1a, but now an upper threshold value X1 and a lower threshold value Y1 are employed which limit an uncertainty range U1 in which the bank notes neither are classified as clearly fit nor as clearly unfit. In the case of the fitness criterion K1, a high fitness measurement value M1 indicates the presence of an unfit bank note. If the fitness measurement value M1 is above the upper threshold value Y1, the bank note is categorizedwith respect to the particular fitness criterion K1as clearly unfit (unfit degree 1), below the lower threshold value X1 as clearly fit (unfit degree 0). For fitness measurement values being in the uncertainty range U1 between X1 and Y1 the unfit degree is between 0 and 1. The value of this unfit degree depends on the course of the selected unfit function F1. In the example of FIG. 2b, for the fitness criterion K1 there was employed a linear, monotonously rising course of the unfit function. However, there can alternatively also be employed an unfit function F1 which has in the uncertainty range U1 a nonlinear, monotonously rising course, e.g. an S-shaped course, cf. FIG. 2c. The nonlinearity e.g. can be advantageous when in the overlapping region of the two frequency distributions the frequency curves behave in a nonlinear fashion.

[0100] In FIG. 3a-b there is shown an example of a different fitness criterion K2, in which a low fitness measurement value M2 indicates the presence of an unfit bank note. Accordingly, the frequency distribution of the unfit bank notes has relatively low fitness measurement values M2 in comparison to the frequency distribution of the fit bank notes. Accordingly, there is employed an unfit function with a reversed course, i.e. which in the uncertainty range U2 monotonously drops from 1 to 0. If the fitness measurement value M2 is above the upper threshold value Y2, the bank note is categorizedwith respect to the fitness criterion K2as clearly fit (unfit degree 0), below the lower threshold value X2 as clearly unfit (unfit degree 1). Here too, the unfit function has a nonlinear course in the uncertainty range.

[0101] In FIG. 5a-c there are shown by way of example three unfit functions F2, F2, F3 for three different fitness criteria which are characterized by the uncertainty ranges U1, U2, U3 and the threshold values X1, Y1, X2, Y2, X3, Y3. FIG. 5a shows the unfit function F1 for a fitness criterion which relates to the damage of the bank note, as a fitness measurement value the damaged area of the bank note being employed here. FIG. 5b shows the unfit function for a fitness criterion F2 which relates to the soiling of the bank note, as a fitness measurement value the remission intensity of the bank note in one or several ROIs being employed here. In FIG. 5C there is shown the unfit function F3 for a fitness criterion which relates to the limpness of the bank note, as a fitness measurement value the ultrasonic intensity transmitted through the bank note being employed here.

[0102] The damaged area is e.g. the sum of all damaged areas of the particular bank note (damages like holes, tears, dog-ears etc.) as they result from a picture of the bank note taken with an optical sensor with the aid of known image processing methods. The remission is measured e.g. in one or several spectral channels in one or several ROIs on the bank note in which the soiling of the particular bank note is checked. The limpness is detected e.g. with the aid of an ultrasound-transmission measurement.

[0103] Moreover, in FIG. 5a-c there are exemplary stated the fitness measurement values M for these three fitness criteria for three bank notes A, B and C, as symbols there being employed for the bank note A the black circle, for bank note B the white circle and for the bank note C the cross. From the particular fitness measurement value M there results for each individual bank note A, B, C from the particular unfit function F1, F2, F3 respectively an unfit degree G1, G2, G3.

[0104] In the table of FIG. 6a, the particular unfit degrees G1, G2 and G3 are plotted for these three bank notes A, B and C. With respect to damages, the bank note A is assigned an unfit degree G1 of 0.80 because of its damaged area, the bank note B an unfit degree G1 of 0.40, and the bank note C an unfit degree G1 of 0. With respect to soiling, the bank note A is assigned an unfit degree G2 of 0 because of its remission, the bank note B an unfit degree G2 of 0.75, and the bank note C an unfit degree G2 of 1. With respect to limpness, the bank note A is assigned an unfit degree G3 of 0.7 because of its ultrasound measurement value, the bank note B an unfit degree G3 of 0, and the bank note C an unfit degree G3 of 0. In the example of FIG. 5, into the unfit degrees G1, G2 and G3 there can respectively also be incorporated several fitness measurement values, e.g. for the soiling check there can be defined several ROIs on the bank note, the fitness measurement values thereof can then be aggregated into one single fitness measurement value, e.g. by adding up, where applicable with different weighting, or multiplying, where applicable with exponents k1.

[0105] For each individual bank note, the particular unfit degrees G1, G2, G3 are now combined into an unfit probability P. For this, the unfit degrees can e.g. be multiplied with each other according to the following formula, in which the exponents k.sub.1=k.sub.2=k.sub.3=1 were set:

[00002]$\begin{array}{cc}P=1-\underset{j}{.Math.}.Math.{\left(1-G_j\right)}^{k_j}=1-{\left(1-G.Math..Math.1\right)}^{k_1}.Math.{\left(1-G.Math..Math.2\right)}^{k_2}.Math.{\left(1-G.Math..Math.3\right)}^{k_3}==1-\left(1-G.Math..Math.1\right).Math.\left(1-G.Math..Math.2\right).Math.\left(1-G.Math..Math.3\right)& \left(2\right)\end{array}$

[0106] This multiplication ensures that a bank note which has an unfit degree of 1 in at least one fitness criterion will altogether get an unfit probability of 100%, independent of the unfit degrees which this bank note has in the other fitness criteria. For example, the soiling unfit degree G2=1 for the bank note C leads to an unfit probability of the bank note C of P=100%, irrespective of how low the unfit degree for limpness and damage may be.

[0107] In FIG. 6b, there are shown the unfit probabilities P calculated in this way for the three bank notes A, B and C and a fitness threshold T usable for the fitness classification thereof, e.g. T=90%. Since the unfit probabilities P of the bank note B are below the fitness threshold T=90%, bank note B is classified as fit. Since the unfit probabilities P of the bank notes A and C are above the fitness threshold T, the bank notes A and C are classified as unfit and can be sorted out by the bank note processing machine. Additionally, for the fitness class ATM-fit there can be employed a further fitness threshold T which is below the fitness threshold T, i.e. for being classified as ATM-fit the bank notes need an even lower unfit probability. For example, for this, the unfit probability P is compared with the further fitness threshold T.

[0108] In the fitness check of the bank note stack 10 to be checked, an unfit probability P is determined for each of these value documents. This unfit probability P is compared with a fitness threshold T which is for the overall state of the value documents. This fitness threshold T can be specified by the user or prior to the value document check, e.g. upon adaptation, or also by remote access from a central place. With a defined fitness threshold there then results from the number of the bank notes whose unfit probability P exceeds this fitness threshold T a corresponding unfit portion, e.g. 20%.

[0109] However, it can also be provided that the user states, by means of the user interface 45, a desired unfit portion for bank notes to be classified as unfit, e.g. in percent. If for example not 20% but only 10% of the bank notes of the bank note stack 10 are to be categorized as unfit, the fitness threshold T is changed such that only 10% of the bank notes exceed the fitness threshold. For achieving this, starting out from the fitness threshold T.sub.20 which has led to a 20% unfit portion, the evaluation device would then set the fitness threshold T accordingly higher (T.sub.10)taking into account the frequency of the unfit probabilities in this bank note stack. Where applicable, the bank notes of the bank note stack 10 can subsequently be checked anewwith the fitness threshold T.sub.10and sorted according to their fitness.

[0110] For defining the unfit functions, the procedure may be as follows: Prior to the fitness check of a bank note stack to be checked, the user selects a first group of bank notes which he classifies as fit, i.e. these bank notes have e.g. at most a low soiling and/or damage which is not felt to be disturbing, and a second group of bank notes which he categorizes as unfit, i.e. these bank notes have striking features like soiling, damage, clips, adhesive tape, etc. By means of the user interface 45 the user selects a configuration operating mode of the bank note processing machine 1, in which parameters for the fitness check can be adjusted, in particular which fitness criteria are to be employed for the fitness check, and/or in which the unfit functions and their threshold values can be defined or changed.

[0111] In the configuration operating mode the user is prompted for example to first insert the bank notes which he has categorized as unfit into the input pocket 20. The bank notes categorized as unfit are grasped singly by the singler 25 and transferred to the transport system 30. The measuring device 41, or the sensor or sensors contained therein, determine measurement data representative of the particular bank note which are transmitted to the evaluation device 40. After all the bank notes categorized as unfit were processed, the user is prompted to insert the bank notes categorized as fit into the input pocket 20 which then are processed analogously to the bank notes fit for circulation. Alternatively, in the operating mode for defining the threshold value or threshold values the unfit and the fit bank notes can also be inserted together into the input pocket 20, if these can be clearly separated from each other by the bank note processing machine 1, e.g. by means of a separation card which is included between the unfit and the fit bank notes. During processing, the separation card is recognized by the control device 40 with the help of the measurement data of the measuring device 41, so that the separation between unfit and fit bank notes can be performed by the control device 40.

[0112] The parameters for the fitness check are then adjusted with the help of the frequency distributions of the fitness measurement values of the fit and the unfit bank notes. This can be effected manually by the user (operator, adapteur, service person), but also automatically by the evaluation device of the value-document processing apparatus. For example, the first threshold value is set to a fitness measurement value at which the fit frequency is much higher than the unfit frequency (e.g. at least has a certain ratio, e.g. 5:1 or 10:1) and the second threshold value is set to a fitness measurement value at which the fit frequency is much lower than the unfit frequency, e.g. has at least a certain ratio (e.g. 1:5 or 1:10). Then the uncertainty range accordingly is in the overlapping region of the two frequency distributions.

[0113] For reducing the number of fitness criteria which must be adapted by a user, several fitness criteria can be aggregated, e.g. several fitness criteria relating to the damage of the bank note. For example, the damaged area can be employed as a fitness criterion K3 and the tear length of the particular bank note as a fitness criterion K4. The fitness measurement values M3 and M4 of the two fitness criteria are aggregated e.g. by a linear combination into the fitness measurement value MK=a*M3+b*M4, into which the fitness measurement values M3 and M4 may be included with different weighting a, b. The result of the linear combination delivers the combined fitness measurement value MK. In FIG. 7a, there can bee seen the distributions of the two fitness measurement values M3 and M4 for a group of unfit bank notes which are respectively represented by a black circle, and for a group of fit bank notes which are respectively represented by a white circle. Moreover, a two-dimensional region clearly fit is shown in which the group unfit degree is 0, and a two-dimensional region clearly unfit in which the group unfit degree is 1. The two threshold values X and Y are formed, in the two-dimensional case, by the two straight lines a*M3+b*M4=X and a*M3+b*M4=Y. A bank note in which there is a*M3+b*M4<X (i.e. MK<X) is categorized, with respect to the combined fitness measurement value MK, as clearly fit (group unfit degree=0), a bank note in which there is a*M1+b*M2>Y (i.e. MK>Y) is categorized, with respect to the combined fitness measurement value MK, as clearly unfit (group unfit degree=1), a bank note in which there applies X<a*M1+b*M2<Y (i.e. X<MK<Y) is in the uncertainty range U in which it has, with respect to combined fitness measurement value, a group unfit degree between 0 and 1.

[0114] In FIG. 7b it is shown how for the aggregated fitness measurement value MK, which was aggregated from the group of the fitness measurement values M3 and M4, the group unfit degree G can be determined. For this, for the combined fitness measurement value MK there is formulated an unfit function F according to the invention with two threshold values X, Y and uncertainty range U in between. With the aid of the unfit function of FIG. 7b there results the group unfit degree G. The unfit probability P of the particular bank note then results from combining the group unfit degree G, which relates e.g. to the damage, with one or several other unfit degrees of individual fitness criteria and/or with one or several other group unfit degrees, e.g. with a group unfit degree relating to the soiling of the bank note. The combination of all unfit degrees is effected e.g. by multiplication of these unfit degrees according to formula (1) or by linear combination.

[0115] Since by aggregating the fitness measurement values into one single fitness measurement value the number of fitness measurement values is reduced, thus the complexity (dimensionality) of the fitness check is reduced. This simplification of the fitness check is easier to comprehend and clearer for the user of the bank note machine. Thus it becomes easier for a user to perform manual adaptations of the severity of the fitness check.