Patent Publication Number: US-8126204-B2

Title: Method of processing mailpieces, the method including graphically classifying signatures associated with the mailpieces

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a 35 U.S.C. §371 National Phase Application from PCT/FR2008/050885, filed May 22, 2008, and designating the United States, which claims the benefit of France Patent Application No. 0755342, filed May 30, 2007. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention relates to a method of processing mailpieces, which method, during a first mailpiece sorting pass, comprises forming a digital image of the surface of each mailpiece that bears information blocks, in deriving a digital signature as a kind of graphical fingerprint from each image associated with a mailpiece, which signature is a logical identifier for the mailpiece, and in recording the signature associated with the mailpiece in a memory in correspondence with sorting information, and, during a second mailpiece sorting pass, comprises forming a digital image of said surface of each mailpiece again in order to derive a current digital signature associated with the mailpiece, and in searching through the signatures recorded in the first sorting pass for a match with said current signature in order to retrieve the sorting information by association. 
     2. Discussion of the Background Art 
     Such a method is known from French Patent Document FR-2 841 673. With that method, it is no longer necessary to affix Id tags constituted by identification codes or time stamps on the surfaces of the mailpieces. The mailpieces are identified by means of respective “virtual” codes that offer the advantage of obviating the need to use bar code printers and thus of significantly reducing the operating and maintenance costs for postal sorting equipment. 
     In that known method, each digital signature comprises a first component or “image component” representative of physical characteristics of the digital image of the mailpiece corresponding to a second component or “postal component” indicating at least one spatial position of the information blocks present in the image of the mailpiece. In particular, the image component is formed by “global” attributes that are representative of overall physical characteristics taken from the entire set of picture elements (pixels) of the digital image of the mailpiece. The image component is also formed of second attributes or “local” attributes that are representative of local physical characteristics taken from distinct portions of a grid applied over the image of the mailpiece (or of a plurality of different grids). 
     In practice, when searching for a match between a current signature and a candidate signature recorded in a signature database for the purpose of retrieving sorting information, firstly the respective image components of the signatures are compared and then the respective postal components of the signatures are compared. 
     When batches of mailpieces coming from bulk senders or “bulk mailers” are to be sorted by using such virtual identification codes or signatures, the problem arises that, overall, the image components (global and local attributes) of the signatures associated with the mailpieces from the same sender cannot serve to discriminate between the signatures. The mailpieces coming from the same bulk sender are generally identical graphically: same type of envelope, same logo printed on the envelope, same sender address block, same position of the recipient address block, etc. Therefore, in practice, it is possible to distinguish between two signatures of mailpieces from the same bulk sender only by the contents of their respective recipient address blocks. 
     However, in forming signatures, it is not impossible that situations might arise in which the data processing system identifies a sender address block instead of a recipient address block. As a result, when searching through the candidate signatures associated with mailpieces from the same sender, it is possible that a match might be found between two signatures that have the same image component and that, in addition, have postal components that are identical due to the fact that they both erroneously identify sender address blocks instead of recipient address blocks. 
     In such a situation, an increase in signature matching errors can be observed when searching for matches. Such matching errors naturally give rise to errors in directing the mailpieces to the sorting outlets, and thus to additional costs for the mail handling performed with a view to delivering the mail. 
     SUMMARY OF THE INVENTION 
     An object of the invention is thus to propose a method of processing mailpieces that is more robust and that makes it possible to reduce the above-indicated matching errors, more particularly in situations in which the mailpieces to be sorted come from bulk senders and thus have very great graphical similitude. 
     To this end, the invention provides a method of processing mailpieces, which method, during a first mailpiece sorting pass, comprises forming a digital image of the surface of each mailpiece that bears information blocks, in deriving a digital signature from each image associated with a mailpiece, which signature is a logical identifier for the mailpiece, and in recording the signature associated with the mailpiece in a memory in correspondence with sorting information, and, during a second mailpiece sorting pass, comprises forming a digital image of said surface of each mailpiece again in order to derive a current digital signature associated with the mailpiece, and in searching through the signatures recorded in the first sorting pass for a match with said current signature in order to retrieve the sorting information by association, said method being characterized in that it further comprises the following steps:
         grouping together the signatures into signature categories or graphical classes on the basis of a certain criterion of graphical similitude so as to compute a mean signature value for each graphical class;   analyzing, for each graphical class in question, activity of the mean signature value for the purpose of detecting significant activity of an information block in the digital images; and   using the results of this activity detection for the purpose of searching for a match.       

     The basic idea of the invention is thus to classify the successive signatures on the basis of graphical models of signatures, which models are consolidated in real time on the fly so as to reflect, through each model, the unchanging graphical appearance of the images of the mailpieces whose signatures come under the model in question. The analysis of activity (or low-frequency change) that is performed each time the model is consolidated or updated when a new signature is put into the graphical class corresponding to said signature model makes it possible to reveal the position of the recipient address block in the images of the mailpieces because the recipient postal address block is probably the block that presents the most significant low-frequency activity, unlike the other information blocks that are normally stable when the mailpieces are from the same bulk sender. 
     The method of the invention may present the following features:
         the signatures are grouped together into graphical classes on the basis of a certain criterion of graphical similitude of their image components for the purpose of computing a mean value for the image components of the signatures coming under the graphical class in question;   the activity of the mean value of the image component is analyzed for the purpose of detecting significant activity of an information block in the digital images;   the results of this detection are used for the purpose of comparing the postal components of the signatures;   global attributes and local attributes of the signatures are used to establish signature similarity.   thresholding of the normalized vector distance is performed on the global attributes so as to establish said similarity;   a correlation coefficient is computed on the local attributes so as to establish said similarity;   a topological analysis method of the “K-means” type is used to analyze the activity of the local attributes of the image component; and   the graphical classes are generated on the fly in the second sorting pass on the basis of the successive current signatures.       

     The method of the invention can be implemented with any type of mailpiece such as letters, or flat articles of small or large format. The method of the invention is also applicable to sorting parcels and other articles identified by virtual identification codes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       An implementation of the method of the invention is described in more detail below with reference to the drawings. This description is given merely by way of example, the example being given by way of indication and in no way limiting the invention. In the drawings: 
         FIG. 1  is a highly diagrammatic view of a mailpiece bearing a plurality of information blocks; 
         FIG. 2  is a highly diagrammatic view of a sorting machine for implementing the method of the invention; 
         FIG. 3  is a flow chart showing how a known sorting method proceeds during a first sorting pass; 
         FIG. 4  is a highly diagrammatic view showing how the image component of the signature of a mailpiece is extracted; 
         FIG. 5  is a highly diagrammatic view showing how the postal component of the signature of a mailpiece is extracted; 
         FIG. 6  is a flow chart showing how a first implementation of the method of the invention proceeds during a second sorting pass; 
         FIG. 7  shows in detail the signature classification step in the method of the invention; 
         FIG. 8  shows how the image components are compared during the signature classification in the method of the invention; 
         FIG. 9  shows an example of updating the image components of the graphical classes in the method of the invention; 
         FIG. 10  shows in detail the step of modifying the postal component of the current mailpiece on the basis of the image component of the corresponding graphical class; 
         FIG. 11  shows in detail the step of modifying the postal component of the current mailpiece on the basis of the postal component of the corresponding graphical class; 
         FIG. 12  is a flow chart showing how a second implementation of the method of the invention proceeds during a second sorting pass; and 
         FIG. 13  shows the principle of reducing the exploration space by means of a prediction mechanism. 
     
    
    
     In the method of the invention, mailpieces such as letters, flat articles or “flats” of small or large format, with wrappers made of paper or of plastics materials, and any other articles to be sorted automatically are identified by digital signatures that are derived from images of the surfaces of the mailpieces that generally bear recipient postal address blocks. Such a digital signature or “image signature” thus serves to identify a mailpiece unambiguously instead of said mailpiece being identified by a bar code time stamp or Id tag in an automatic postal sorting machine. The term “machine” is used to designate, in the broadest sense, postal sorting equipment installed on one or more postal sorting sites optionally including video-coders. 
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Forming the Digital Signatures 
       FIG. 1  shows a mailpiece P whose surface bears, for example, a first information block AD that corresponds to the postal delivery address or “recipient address”, an information block AE that corresponds to a sender address, and an information block L that can take the form of a graphical logo that corresponds to other additional textual information such as an advertising slogan printed by the sender. 
       FIG. 2  is a highly diagrammatic view of a postal sorting machine  1  that conventionally comprises: a feed inlet  2  with a loading zone for loading mailpieces P and an unstacker for putting the mailpieces on edge in series; a digital camera  3  for forming an image of the surface of each mailpiece that bears the various above-mentioned information blocks; and a bucket carrousel  4  that directs the mailpieces to sorting outlets  5 . Each sorting outlet can be equipped with a plurality of sorting outlet bins (typically a front bin and a rear bin) or with a compartmented sorting outlet bin, without going beyond the ambit of the invention. 
       FIG. 2  also shows a data-processing system referenced  6  for postal address recognition by Optical Character Recognition (OCR). Said data-processing system is associated with a video coding system  7  as is well known. In accordance with the invention, the system  6  is also arranged to derive a digital signature from the digital image of a mailpiece that is formed by the camera  3 . 
       FIG. 2  also shows a system referenced  9  that is analogous to the system  6  but that is part of another sorting machine  10 , said system  9  being connected to the system  6  for communication purposes, e.g. via a telecommunications network  8  for applying the method of the invention to a sorting process made up of a plurality of sorting passes through a plurality of sorting machines. 
       FIG. 3  is a highly diagrammatic view showing how digital signatures are generated during a first sorting pass through the machine  1 . In an initial step  11 , mailpieces P are injected into the sorting machine  1  via the feed inlet  2 . The mailpieces P are unstacked and conveyed in series and on edge to the digital camera  3 . 
     In step  12 , a digital image is formed of the surface of the current mailpiece that can bear the various information blocks shown in  FIG. 1 , e.g. AE, AD, and L. 
     In step  13 , the system  6  undertakes automatic recognition of the delivery address by OCR, based on the image, and, at the same time, derives therefrom a digital signature V_Id attributed to the mailpiece. 
     In step  14 , if the postal address information is recognized unambiguously in step  13 , the address data resulting from the OCR address recognition is recorded in a memory in step  16  in correspondence with the digital signature V_Id of the mailpiece. 
     If, in step  13 , the address is not fully recognized by the OCR, i.e. if the OCR gives ambiguous address information, or indeed no result, then, in step  15 , the image of the mailpiece is transmitted to the video coding system  7  for having the address information extracted by a video coding operator, whereupon the address information obtained by video-coding in step  15  is recorded in step  16  in correspondence with the signature obtained in step  13 . 
     In  FIG. 3 , the block referenced  17  represents a database DBref in which, for each mailpiece, the digital signature V_Id and sorting data (including the address information) of the mailpiece recognized by OCR or by video-coding during the first sorting pass are recorded in correspondence. 
     Step  18  corresponds to the process of conveying the mailpiece from the camera  3  to the sorting outlets of the machine. 
       FIGS. 4 and 5  show the components of a signature of the invention in more detail.  FIG. 4  also shows the digital image of a mailpiece P, which is generally a gray-scale digital image, with the information blocks AD, AE, and L. 
     A first component of the signature of the invention is referred to as an “image component” Ci which is representative of the physical characteristics of the image. For example, this image component is extracted by statistically analyzing the luminance of the pixels of the digital image which has previously been subjected to a succession of filtering operations lowering the level of resolution of the image in order to reduce the processing time required for the statistical analysis, and in order to have contents of the low-frequency type which are relatively insensitive to fluctuations in luminance during multiple acquisitions. The luminance of a pixel of the image corresponds to the gray scale value of the pixel. 
     On the basis of the low-resolution digital image of a mailpiece, the system  6  uses computation to extract global attributes that are representative of overall physical characteristics of the image, such as height and width of the mailpiece, mean luminance of the pixels of the digital image, standard deviation, and entropy of the luminance values. 
     It is also possible to use computation to extract local attributes representative of local physical characteristics of the image that are taken from distinct portions of the digital image.  FIG. 4  shows the digital image of the mailpiece P as subdivided into a plurality of distinct portions B 11 , B′ 45  resulting from various grids M 1 , M 2 , M 3 , M 4 , M 5  being applied over the digital image. The grid M 1  defines 3×3 distinct portions in this example. The grid M 5  defines 8×10 distinct portions. The number of distinct portions in a grid and the number of grids can be a parameter in the statistical analysis applied to the digital image for the purpose of extracting the image component Ci of the signature. On the basis of each portion such as B 11  and B′ 45  of the digital image resulting from a grid such as M 1  or M 3 , it is possible to extract local attributes such as the mean luminance value of the pixels in this portion of the digital image, the standard deviation, and the entropy of the luminance values in this portion of the digital image. These local attributes contain discriminatory information, and the more varied the mailpieces, the more discriminatory the information. The entire set of the global and local attributes extracted for a digital image thus constitute the image component Ci of the signature. 
       FIG. 5  shows a “postal component” Cp of the signature that indicates at least the spatial positions of the information blocks such as AD, AE and L in an image of a mailpiece. An OCR system conventionally used in a postal sorting machine is capable of delivering data indicating the spatial positions of blocks of textual information detected in the digital image. Such position-indicating data can be constituted by the spatial and angular positioning coordinates of the rectangular zone forming each information block. An OCR system is also suitable for delivering a textual description of each information block detected in the digital image. For example, a textual description of an information block such as AD can consist in an indication of the number of rows of characters detected in the information block, the number of words detected in each row of characters, or the number of characters detected in each word of each row of characters.  FIG. 5  shows an example of a textual description of the information block AD constituting the postal component Cp of the signature of a mailpiece: 
     “BLOCK #0/3”, designated by  33 , references information block  0  from among the three information blocks detected in the digital image; 
     “HN”, designated by  33 ′, is data indicating the angular positioning of information block 0 in the digital image; 
     “(0684 0626 0895 0756)” designated by  33 ″ are data items representative of the spatial co-ordinates of information block 0 in the digital image; 
     “NbrRows 4”, designated by  33 ″′, indicates that the information block 0 contains four rows of characters; 
     “Row #0”, designated by  34 , references the first row of characters detected in information block 0; 
     “NbrWords 03”, designated by  35 , is data indicating that three words have been detected in the first row of characters; 
     “NbrCharPerWord 01 06 04”, designated by  36 , are data items indicating that the three words of the first row of characters contain 1, 6, and 4 characters, respectively; 
     “char #0 (1 007 I 009 i 019)”, designated by  37 , are data items indicating that, for the first character of the first row of characters, the OCR has identified three candidate characters, respectively 1, I, and i, with respective resemblance distances of 007, 009, and 019; 
     “char #1 (L 008 E 009 D 057)”, designated by  38 , are data items indicating that for the second character of the first row of characters, the OCR has identified three candidate characters, respectively L, E, and D, with respective resemblance distances of 008, 009, and 057; 
     and so on for the other characters of the first row of characters, given that a value 0 for the resemblance distance is the shortest distance, i.e. it represents the smallest departure from the ideal character. 
     Generating a signature thus stems from the idea that a digital image of a mailpiece is an interpretable two-dimensional signal whose contents can be understood both physically and symbolically. Because the signature of a mailpiece is made up of two complementary components Ci and Cp that are not mutually correlated (i.e. that are independent from each other). 
     Searching for Signature Matches in the Second Sorting Pass 
       FIG. 6  shows the process of managing the signatures during a second sorting pass performed after the first sorting pass shown in  FIG. 3 . The mailpieces sorted in the first pass are thus fed back into the sorting machine  1  and go past the camera  3  again in series and on edge. In step  41 , a digital image is formed again of the surface of a current mailpiece that bears information blocks such as the recipient address block AD, and, in step  42 , a current signature V_IdC is derived again for the current mailpiece as indicated above. The current signature V_IdC comprises an image component CiC and a postal component CpC. 
     Then, in step  46 , the image and postal components CiC and CpC of the current signature V_IdC are compared with the image and postal components Ci, Cp of the signatures recorded in the database DBref  17  for the purpose of detecting any match. 
     This comparison can begin with a comparison of the respective global attributes of the image components, which comparison includes thresholding of the absolute values of the variations over each global attribute so as to perform initial filtering from among the signatures recorded in the database  17 . This filtering makes it possible to eliminate the signatures that are very dissimilar from the current signature, and to retain a small number of candidate signatures only for continuing the comparison. 
     Then the local attributes of the image components of said candidate signatures are compared, which comparison can further reduce the number of candidate signatures in the database  17 . This comparison can be based firstly on computing a moving normalized correlation coefficient between the corresponding histograms in the current signature and in the respective candidate signatures, thereby making it possible to be unaffected by any variations in luminance between the two digital images being compared, and then on computing a normalized correlation coefficient per type of attribute, between the other local attributes in the current signature and in the respective candidate signatures, thereby making it possible to be unaffected by problems of normalization due to the difference in variability of each local attribute. The candidate signatures are then sorted in decreasing order of resemblance on the basis of the correlation coefficients and a fixed number of most similar candidate signatures are retained. 
     Comparison between the postal components Cp of the signatures begins by measuring the resemblance of the data indicating the positions of the information blocks. This second comparison advantageously uses a second criterion that is decorrelated from the comparison criterion for the comparison of the image components. These candidate signatures can then be sorted in decreasing order on the basis of a measurement of resemblance between the textual descriptions of the information blocks. 
     Naturally, the comparison of the postal components can be performed before the comparison of the image components, or indeed the postal and image component comparisons can be performed simultaneously without going beyond the scope of the invention. 
     If, in step  46 , it is not possible to detect a match, then, in step  47 , the mailpiece is directed to a reject outlet for manual sorting, for example. If, in step  46 , a match is detected, the sorting and address data for the current mailpiece is retrieved from the database  17 , and the current mailpiece is directed automatically to another sorting outlet corresponding to step  48 . 
     Categorizing the Signatures 
     In the method of the invention, prior to step  46  in  FIG. 6 , a reliability-enhancing process is performed firstly for increasing the reliability of the current signature, and secondly for increasing the reliability of the signatures of the database DBref by means of classifying said signatures. This reliability enhancement makes it possible, during the search for a match, to adjust the postal components of the signatures to be compared so as to avoid matching errors. 
     The principle of this classification is firstly to create a sort of dictionary in which categories or “graphical classes” CG of signatures are listed. 
     Each graphical class is modeled by an image component CiCG and by a postal component CpCG similar to a signature component as described above. 
     In accordance with the invention, this dictionary is updatable in real time in that the image and postal components of the graphical classes are updated on the fly, i.e. as the process proceeds, a signature is attributed to a graphical class, and said graphical class is therefore consolidated. This updating on the fly does not require any deferred processing: it is executed in real time. In addition, with such updating on the fly, it is not necessary to transmit the dictionary for subsequent sorting in a remote sorting centre. 
     As can be understood, in accordance with an important characteristic of the method of the invention, each time a graphical category is updated, a mean value is computed for the image component (consolidation stage), and the low-frequency activity of said mean value is analyzed so as to assess the position of the recipient address block, thereby making it possible, on searching for a match in step  46 , to use the appropriate information block when comparing the postal components of the signatures. The term “low-frequency analysis” is used to mean analysis of the changes in low-resolution images. 
     In step  43 , a process of classifying the current signature VidC is performed, i.e. the image component CiC of the current signature is compared with the image component CiCG of the graphical classes present in the dictionary  19  so as to determine the graphical class CGx of the dictionary that is most similar to the current signature. More particularly, in the method of the invention, the local and global attributes of the image components CiC and CiCG are compared so as to identify a membership graphical class CGx at the end of step  43 . If no graphical class of the dictionary corresponds to the current signature, a new graphical class CGx is created. 
       FIGS. 7 and 8  show more particularly the process of comparing the image components so as to achieve this classification. Those figures are described further on in the description below. 
     In step  44 , which follows the classification step, an analysis of the low-frequency activity of the image component of the graphical class CGx is performed so as to detect any significant low-frequency activity of an information block in the digital images associated with the signatures coming under this graphical class. This step  44  is described more particularly with reference to  FIG. 9 . 
     In step  45  reliability-enhancing processing is performed for the current signature on the basis of the results of the activity analysis at step  44  that is described in more detail with reference to  FIGS. 10 and 11 . At the end of step  45 , the postal components of the current signature and of the graphical class CGx can be adjusted. These adjustments make it possible to avoid matching errors in the step  46  for searching for a match with the signatures recorded in the database  17 . 
     After the step  46  for searching for a match, at  50 , consolidation of the postal component of the graphical class CGx identified for the current mailpiece is performed only if, at the preceding step  49 , the situation is one in which a certain “strong postal criterion” is verified. The term “strong postal criterion” is used to mean a situation in which the postal components CpC of the current signature and of a matching signature identified in step  46  are similar (e.g.: same identification of the recipient address block, said number of rows of characters in that block, same number of words per row in that block, etc.). 
     The process of steps  41  to  50  in  FIG. 6  is repeated in this way for the successive current mailpieces processed in the second sorting pass. 
       FIGS. 7 and 8  thus show in more detail the step  43  for classifying the signatures. On initializing the process, i.e. for the first mailpiece of the second sorting pass, the dictionary or database  19  of the graphical classes CG is normally empty. In order to satisfy real-time processing constraints, it is possible to limit the number of graphical classes CG kept in the dictionary to a certain value NbrMaxCG that can be adjustable. The value NbrMaxCG can, for example, be conditioned by “a priori” information given by the machine operator and indicating the non-uniformity of the mailpieces to be processed. In general, for two-pass sorting of mailpieces coming from a plurality of bulk senders, it is possible to set the value NbrMaxCG to about  100 . A value NbrMaxCG equal to  100  corresponds to a batch of mailpieces that present low uniformity, i.e. with a number of successive mailpieces belonging to the same sender that is relatively low. 
     In  FIG. 7 , in step  51 , the current image component CiC of the current signature V_IdC is compared with the image component CiCG of each graphical class recorded in the database  19  so as to detect a certain amount of similarity. The comparison of the image components is performed in the same way as described above for step  46 . In particular, the comparison is performed firstly on the global attributes and then on the local attributes of the image components CiC and CiCG. The global and local attributes are obtained on the basis of a low-resolution image of the mailpiece, e.g. of resolution 0.25 pixels per millimeter (mm). 
       FIG. 8  shows more particularly this step  51  of comparing the global attributes ( 51   a ) and the local attributes ( 51   b ) of the image components CiC and CiCG. As global attributes,  FIG. 8  shows: the height H of the mailpiece, the width L, the mean luminance value I of the pixels of the digital image, the standard deviation E, and the entropy T of the luminance values. On the left of  FIG. 8 , in the box  52 , a first vector (one-column table) shows the values H, L, I, E, T for CiC, and a few other vectors show the similar values for the CiCGs associated with the classes CG 1 , CG 2 , . . . , CG N . 
     For the purpose of pre-selecting the candidate graphical classes CGi, firstly each global attribute of the image component CiC is compared with the corresponding attribute of one of the image components CG 1 , CG 2 , . . . , CG N . For example, in order to determine whether the graphical class CG 1  is a candidate, the difference between the heights H of CiC and of CG 1  is compared with a threshold τ 1 , the differences between the widths L of CIC and of CG 1  is compared with a threshold τ 2  . . . , it being possible for the thresholds τ 1 , τ 2  . . . to be different, and, if, for all of the attributes, the difference is less than the threshold, then CG 1  is the candidate. 
     A distance (normalized vector distance, designated in  FIG. 8  by NVD) is then computed between the vector HLIET for CiC and the vector HLIET of each candidate graphical class. The normalized vector distances NVDs are also compared with a threshold (which can be adjustable) in step  53  so as to determine the graphical class(es) CGi that are most similar to the current signature. 
     On the basis of the graphical class(es) CGi that are pre-selected in this way, the local attributes of the image component CiCG of said graphical classes are compared with the local attributes of the image component CiC of the current signature.  FIG. 8  shows the local attributes in the box  54  by triplets of 4×4 matrices (thus resulting from application of a 4×4 grid for generating the signature) assigned respectively to the mean luminance values I of pixels, the standard deviation E, and the entropy T of the luminance values. 
     The CiC local attributes are correlated with the CiCG local attributes of each graphical class selected at  53 , and the correlation coefficient CC that is computed (−1≦CC≦1) and that is the highest is chosen at the end of step  51 . Naturally, it is possible to use various sizes of grid in computing the correlation values. 
     Then, in step  55 , said correlation coefficient CC is compared with a threshold (which is optionally adjustable) so as to undertake updating of the dictionary  19  of the graphical classes. Thus, if, at step  55 , the correlation coefficient CC is less than the threshold, then, in step  56 , a new graphical class CGx is added to the dictionary  19 , and the number of graphical classes kept in the dictionary is updated (where applicable, a graphical class can be deleted in the event that the value NbrMaxCG is exceeded, the graphical class deleted from the dictionary being the one that is least used during a reference period, for example). The components CiCG and CpCG of the new graphical class CGx are initialized with the components CiC and CpC of the current signature used in step  48 . 
     If, in step  55 , the correlation coefficient CC is higher than or equal to the threshold, then, at  57 , the counter for counting use of the graphical class CGx is updated, and said graphical class CGx is updated. 
     Adjusting the threshold of step  55  makes it possible to adjust the level of similitude required for the categorization. In practice, it is necessary to find a threshold that makes it possible to bring together the signatures of mailpieces that have considerable graphical similitude while preventing mailpieces from the same sender and having the same graphical appearance from being able to result in a plurality of graphical classes being created. At the end of step  43 , a graphical class CGx is thus identified as being similar to the current signature VidC. 
       FIG. 9  shows a set of local attributes of the image component of the graphical class CGx that serve for low-frequency activity analysis in step  44  in  FIG. 6 . These local attributes are standard deviation values on: mean luminance I, standard deviation E and entropy T. These values relate to distinct zones of a certain grid applied to the image of a mailpiece as explained above. By way of example,  FIG. 9  shows application of a 5×5 grid over the image, giving 3 mtrices  60 , each having 25 standard deviation values. In order to compute each standard deviation value in a matrix  155 , E 55 , and T 55 , a mean is taken that is consolidated with the corresponding value of the local attribute of the current signature. It can thus be considered that, by computing this standard deviation, it is possible to compute a mean signature value for the graphical class CGx. The three matrices I 55 , E 55 , and T 55  can be merged into one matrix G 55 , e.g. using a normalized principal-components analysis (NPCA), well known to the person skilled in the art. This matrix G is a sort of “changes grid” that shows the low-frequency changes, i.e. the changes that occur each time the matrix G is consolidated. 
     At  62 , a summary of the matrix G is shown in which the matrix elements are distributed into three classes by a topological method such as a non-supervised method of the “K-means” type based on measuring proximity in space of the observations. The “K-means” method makes it possible, in the matrix G, to isolate matrix elements on the basis of 3 levels: unchanging; changing little; and highly changeable; these levels being shown respectively by white, gray, and black squares. In order to be unaffected by variations related to the mechanical acquisition fluctuations, it is possible not to take account of the squares detected as being “changeable” that are on the edges of the matrix G. It is possible to group the squares together as a function of their relatedness and of their low-frequency activity. It is possible to use a plurality of matrices G having different grids. At the end of step  44 , the spatial position of an information block that has the most significant low-frequency activity has normally been detected. 
       FIG. 10  shows how the result of the low-frequency activity is taken into account in step  45 . At  63 , if low-frequency activity of a matrix element of a matrix G (or of matrix elements of a plurality of matrices G) has/have been detected by detecting the presence, for example, of a black square (highly changeable block at  62 ), then, at  64 , it is verified whether the position of said square in the matrix G coincides with the position of the information block in the postal component CpC of the current signature that has been chosen as the recipient address block. Whether or not it coincides can be established by a spatial projection of the co-ordinates of the square and of the information block so as to measure a spatial distance and then by comparing said measured distance with a threshold. In the event that such coincidence is detected, the process continues at step  70  in  FIG. 11 . 
     If, in step  63 , no low-frequency activity has been detected, the method continues at step  70  in  FIG. 11 . 
     If, at step  64 , no spatial coincidence is detected between the information block identified in the postal component CpC of the current signature and “the block” of the matrix that shows the low-frequency activity that is most significant, then, in step  65 , a measurement is taken of the spatial coincidence between this “block” that shows the highest low-frequency activity and all of the other information blocks identified in the postal component CpC of the current signature. If no spatial coincidence with said other information blocks is detected, the process continues at step  70  of  FIG. 11 . 
     If, in step  65 , a spatial coincidence is detected with one of the other information blocks, then, in step  66 , the postal component CpC of the current signature is modified so as to identify said information block as being probably the recipient address block, said block thus being used subsequently in step  46  for searching for signature matching. 
     As shown in  FIG. 11 , in step  70 , a consolidated measurement is taken on the dispersion of the spatial position of the information block identified as being the recipient address block in the postal components of all of the successive signatures forming the graphical class CGx. This dispersion measurement is in the form of a standard deviation value. If it is detected that the distance measured is greater than a certain threshold, the process continues at step  46  of  FIG. 6 . This is the situation in which the postal component CpC of the graphical class CGx is not reliable enough due to the fact that the signature postal component that served to generate this graphical class is unstable. 
     Conversely, if, at step  70 , the distance measured is less than said threshold, the process continues at step  71  in which spatial matching is measured between the postal component of the graphical class CGx and the postal component CpC of the current signature. This measurement consists, for example, in computing the difference in position between the center of the information block identified as the recipient address block in the postal component of the current signature and the mean position of the center of the recipient address block of the corresponding graphical class. This measurement is compared with a low threshold and, if said measurement is less than this certain low threshold, the process continues at step  46  of  FIG. 6  for searching for signature matching. 
     If, in step  71 , no spatial matching is detected, then the process continues at step  72  in which a search is made to determine whether there exists an information block identified in the postal component CpC of the current signature and for which the distance from the information block identified as being the recipient address block of the postal component of the class CGx is less than the low threshold. 
     If the answer to step  72  is “yes”, the process goes on to step  73  in which the information block is then identified in the postal component CpCG of the graphical class CGx as being probably the recipient address block. 
     Conversely, if the answer to step  72  is “no”, the process continues at step  74  in which the spatial distance is measured between all of the information blocks identified in the postal component CpC of the current signature and the information block identified as being the recipient address block in the postal component of the graphical class CGx. If that distance is greater than a high threshold, the postal component of the class CGx is re-initialized (Reset) in step  75  in the dictionary  19 . 
     In  FIG. 12 , another implementation of the method of the invention is shown that differs from the implementation shown in  FIG. 6  at step  42 . In the step  80  that follows the step  42  in which a current signature VidC is generated, the current signature is classified by means of a dictionary  19  as explained above. A similar graphical class CGx is retrieved. The postal component CpC of the current signature is, where appropriate, modified, on the basis of the image component CiCG of the graphical class CGx, and, where appropriate, the postal component CpCG of the graphical class CGx is modified. 
     In step  81 , candidate signatures are retrieved from the signature database  17  using a sequence prediction mechanism that is known from French Patent Document FR 2 883 943. Step  81  serves to limit the exploration space in the database  17  by making advantageous use of account being taken of a certain sequencing (pass order) for the mailpieces in the first sorting pass that is repeated in the second sorting pass. This limited exploration space is constituted by candidate signatures. 
     In step  82 , each candidate signature is classified in the dictionary  19  of graphical classes using the above-defined classification mechanism. At the end of step  82 , one graphical class CGy has been identified for each candidate signature. 
     If, in step  83 , it is detected that the graphical class CGx of the current signature is identical to the graphical class CGy of the candidate signature in question, then, in step  84 , the postal component Cp of the candidate signature is modified using the mechanism described with reference to  FIGS. 10 and 11  so as to reposition the recipient postal address properly in the postal component of the candidate signature. Then, in step  85 , where appropriate, the postal component of the candidate signature is modified as a function of the postal component of the graphical class CGx as described with reference to  FIG. 11 . The method then continues at step  46  of  FIG. 6 . It should be understood that the process from steps  82  to  85  is repeated for each candidate signature at the end of step  81 . 
     If, in step  83 , no common graphical class has been detected that is common to the current signature and to the candidate signatures, the process continues directly at step  46  of  FIG. 6 . 
     The method of limiting the exploration space of step  81  is based on the idea of attributing a chronological serial number SN to each mailpiece in the first sorting pass, which number is recorded in a memory in correspondence with the signature of the mailpiece in the database  17 . Each chronological serial number SN can, for example, be constituted by the juxtaposition of a sorting center number assigned to the sorting center in which the sorting machine  1  is located, of a sorting machine number assigned to the sorting machine in which the mailpiece is sorted, of a sorting outlet bin number assigned to the sorting outlet bin to which the mailpiece is directed, and of a chronological index assigned to the mailpiece. In practice, said index is, for example, the value of a counter which is associated with a sorting outlet bin, which is initialized when a first mailpiece is directed to the bin, and which is incremented by one unit every time a new mailpiece is directed into the bin. In this manner, a serial number SN that is unique is assigned to each mailpiece. 
     At the end of the first sorting pass, the signatures are grouped together in a sequence in the database  17 . For example, the signatures are grouped together and sequenced by sorting center, by machine, and by bin in the order in which the mailpieces are stored in each sorting outlet bin. As described in detail below, the sequences of contiguous signatures in the database  17  constitute identifiable segments. 
     During the second sorting pass, at the end of step  80  in  FIG. 12 , a pass index PI (ranging from 1 to n) is attributed to the signature V_IdC of the current mailpiece. Whereupon, an estimated chronological serial number SN is computed for the current signature by linear approximation as described in French Patent Document FR-2 883 943. This computation is performed by linear approximation on the basis of a series of chronological serial numbers stored in the memory. On a graph in  FIG. 13 , pass indices PI for mailpieces  374  to  405  are plotted along the x-axis, those indices corresponding respectively to the second pass of mailpieces  374  to  405  for which digital signatures VId have been extracted in step  80 . Examples of chronological serial numbers SNs assigned to mailpieces in the first pass (mailpieces stored in bins numbered “76” and “86” in this example) are plotted up the y-axis. The computation by linear approximation consists, on the basis of a series of mailpiece pass index and chronological serial number (PI,SN) pairs shown by crosses in  FIG. 13 , in determining by an equation system the coefficients a, b of a straight line (SN=a.PI+b) such as D 1  or D 2  so as then to be able to compute a chronological serial number SN placed on said straight line as a function of a current pass index PI. 
     In addition, the implementation of the method of the invention that is shown in  FIG. 12  makes advantageous use of the above-described prediction mechanism during the consolidation step  50 . In step  50 , the postal component of the graphical class CGx identified for the current mailpiece is consolidated if, in step  49 , the situation is a “strong postal criterion” situation, as explained above, and if, in addition, the chronological serial number of the matching signature determined in step  46  corresponds to the estimated chronological serial number SN.