Process and equipment for recognition of a pattern on an item presented

Abstract: A novel system for recognition of patterns on presented item. A known recognition system expanded by a complementary recognition which is linked with the primary recognition. The results of the primary recognition which are not positively recognised are passed on to the complementary recognition and any characters not positively recognised by the complementary recognition are again passed on to a correction system. Here an operator classifies the unrecognised characters which are then used to teach the complementary recognition unit. Thus, the classified data of the correction system provide the training data for a continuous training process which is coupled with the correction system.

Description: Scope of the invention The invention deals in general with a process for recognition of a pattern on an item presented. In particular, it relates to any such process involving a continuous pattern adaptation process. The invention further relates to equipment to execute such a process Background of the invention The recognition of characters or character patterns on presented items represents a function of ever-growing importance in the modern business world. Particularly in the field of banking and other financial activities, data processing equipment with character recognition units are employed in order to facilitate processing of transfer instructions, pre-printed cheques and other vouchers without manual assistance. Such recognition units are also employed in automatic letter sorting. For such purposes one uses a specified set of identifiers, such as the alphanumeric set of characters including certain special characters. The recognition process is then used to allocate an identification giving maximum possible reliability to a character for recognition. To do this, after detailed pre-processing of the subject data in an initial stage of a classifier for the item to be recognised, numerical evaluations known as credibility factors are estimated for one or several identifiers for consideration, which in a subsequent stage are employed as the basis for decision regarding allocation of the item to an identifier. These allocation decisions are subject to residual faults, produced through rejects and acceptance of inaccurate identifiers (substitutions). The desire for the minimum possible rejection and simultaneous minimum substitution proportion involves contradictory requirements of the automated recognition process. State of the art In order to improve reliability of pattern recognition, experiments have been made using what is known as the "multi-voting" process, in which the reading results for the same character from several pattern recognition units are taken to what is known as a "narrowing unit", which compares the results and selects the overall result in accordance with the following rules: a) If all the recognition units produce the same result, the overall result can be selected from any recognition unit desired; b) If none of the recognition units is able to provide a reliable result, the overall result is a "reject" (not recognisable); c) If the results of all the recognition units are the same, but among them there is at least one with a reliability factor which is greater than a previously defined threshold value (e.g. 50%), then the most reliable value is selected as the overall result; d) If the results of all the recognition units are disparate, the overall result is a "reject" (not recognisable). From DE 41 33 590 A1 we know of a process for classification of such signals, which in each case represents one of several possible amplitude values of the signals. In this process, the following working stages take place in parallel in one or more channels: i) Samples are formed from several scanned values in each case; ii) At least one characteristic is extracted from each sample; iii) The characteristic or characteristics extracted from each sample are used as addresses, so that one can read out from a table occurrence probabilities which are stored in it. For further evaluation, from the occurrence probabilities of all samples a decision dimension is calculated and compared with a prescribed threshold value. DE 21 12 919 B2 sets out a further arrangement for recognition of characters using the multi-voting process. This contains an initial character processing path, which has a collection array, which takes information from the characters, a processing array, which receives the signals from the collection array for processing and a decision array, which receives the signals from the processing array and in which a character is recognisable, causing a decision signal to be displayed at its outlet. In addition, a second character processing path is used, which consists on the one hand of a further collection array and/or a further processing array and the decision array already present, or on the other hand, in each case of a second collection, processing and decision array. At least one of the three arrays of the second character processing path works in accordance with a different principle from the corresponding array of the first character processing path, and at the outlet of the second character processing path a decision signal together with a decision signal from the first character processing path is fed into a comparison array, which produces a recognition signal if the two match. The multi-voting process further requires the use of at least two recognition units, each employing a different recognition algorithm, but it only slightly improves the reliability of the recognition process compared with previous processes. From DE 44 07 998 C2 we know of a process for recognition of a pattern on a voucher, where at least two different pattern recognition units are used to recognise the pattern. Means are also provided to determine a credibility factor represented by an unsharp variable for each pattern recognised by the pattern recognition units, as well as means to evaluate the patterns recognised with the aid of the specified credibility factors. DE 44 36 408 C1 describes a pattern recognition process, in which in a training phase a calibration specification is produced for the valuations for possible identifiers proposed by a classifier, and in the recognition procedure the valuations estimated by the classifier are replaced by different values using the calibration specification. Finally, JP-A-8235304 describes a character recognition apparatus with a first and a second recognition unit, in which the second recognition unit has a supplementary dictionary as well as a monitoring unit. The extracted characteristic is fed to the dictionary in accordance with a correction character prescribed by the user, and character recognition is performed again. Once the second character recognition is completed, the additional dictionary is initialised. In most cases present day pattern recognition systems contain, in addition to appliances for optical picture prescribed by the user, amd character recognition is performed again. Once the second character recognition is completed, the additional dictionary is initialised. US-A-5,257,328 discloses a document recognition device capable of correcting results obtained by recognizing a character image. The document recognition device includes a character recognition unit for recognizing a character image, a correction data base in which correction information on mis-recognized characters which were targets to be corrected by an operator has been registered, an automatic correction unit for correcting results recognized by the character recognition unit by use of the correction data base, an operator's correction unit by which the operator corrects results corrected by the automatic correction unit, and a correction data base updating unit. In US-A-5,251,269, an apparatus and method for recognizing written items including chraracters and symbols is disclosed. The mechanism includes a character recognition rule base which yields identified and unidentified characters. The unidentified characters are conveyed to neural networks for recognition. In US-A-5 251 268 is disclosed a two stage character recognition system in which characters unidentified by a first recognition module are passed on to a neural network which is specifically trained to deal with misrecognised characters. Between both recognition stages there is a vectoriser which segments the character sequence and derives a feature vector on which the neural network operates. In most cases present day pattern recognition systems contain, in addition to appliances for optical picture recording and the actual recognition units, a correction station, which for postal applications may be a video coding system, on which rejects are displayed and are manually corrected by people specially trained to do so. The corrected data, i.e. the video pictures of the unrecognised or inadequately recognised characters (non-coded information, NCI), together with the relevant manually inputted correct characters (coded information, CI) are in this case not further used for recognition purposes, but only for correction purposes. In the development of a pattern recognition system, large quantities of character patterns are collected in a training database. They must be representative of the proposed recognition task. In the next stage, the characters are digitalised using an optical scanner (NCI data) and allocated entirely to the desired character categories, e.g. letters, numbers or even special characters. This section is also called the training process. The quality of character recognition depends to a large extent on this training database. If the data are 0 not representative 0 incomplete in terms of character form categories 0 out of line with reality in the distribution of character form 0 obsolete, as regards new fonts or trendy handwriting 0 obtained from a different optical recording system, this produces unsatisfactory recognition results, although the training process has been performed correctly. The training processes are expensive, since a number of steps are necessary to undertake them, e.g. 0 collection of patterns 0 assessment of their representativeness 0 scanning in of patterns 0 manual allocation of characters to a character category 0 testing of the new classifiers. Consequently, they are performed as seldom as possible. It is expected that there will be about one year or more between two versions of recognition software. Also the collection of data necessary is a problem, as genuine data is often confidential or personal, e.g. in the case of letters. Aim of the invention Consequently, one aim of the present invention is to provide a process for recognition of characters which enables an improved rate of recognition to be achieved. A further aim of the invention is to provide a recognition process enabling the relevant recognition systems to be learnt. It is again an aim of the present invention to provide apparatus enabling the above process to be performed. These and other aims are achieved by the apparatus according to claim 1 and the process according to claim 5. Other advantageous embodiments of the invention are set out in the sub-claims. Brief description of the drawings Further details of the invention are given hereafter using the drawings. Figure 1 shows a depiction in diagram form of the overall recognition system as in the invention; Figure 2 shows in diagram form the principle of the training process as in the invention; and Figure 3 shows an example of form categories of a character in a matrix. Detailed description of the invention Figure 1 shows in diagram form the structure of the overall recognition system as in the invention. It consists of at least two recognition sub-systems, what is known as the primary recognition 1 and after this a component 3, which has been trained to recognise in particular those characters which the primary recognition has not recognised or only poorly recognised. Hence, this subsequent component uses a set of characters which is complementary to the primary recognition and it is therefore designated complementary recognition. In addition, it must contain matrices with form categories, so that it is able to recognise, whether for example a new trendy character form has appeared. Figure 1 shows only two recognition sub-systems 1 and 3. However, the expert will realise that it is possible to have other such sub-systems, either inserted between the primary recognition and complementary recognition or, following the complementary recognition. The primary recognition has all the features of a normal recognition system in which classifiers for the individual text are determined by a pattern adaptation process. These classifiers contain characteristics of different types of writing, which are to be recognised in the recognition process. Thus, the primary recognition consists of 0 the recognition algorithms and 0 the classifiers. Classifiers have a considerable influence upon the recognition result. When the primary recognition is used, it provides a confidence figure for the degree of similarity of a character with a character pattern from the pattern adaptation process. If the confidence figure for a proposed character is high, the risk of substitution is low. If the confidence figure drops, there is an increased risk that a character will be incorrectly recognised. All classified characters of the primary recognition 1 having a confidence figure below a prescribed threshold value are now passed to the complementary recognition 3. Characters which are also unrecognised or only erroneously recognised by the complementary recognition (rejects) are then passed on to a correction system 5, e.g. a video coding system. The rejects which are manually corrected in this correction system now normally form the end result of the character recognition (arrow 6 in figure 1). The recognition system as in the invention now has an additional pattern adaptation system 7, coming after the correction system 5 and arranged between the correction system and the complementary recognition 3, making it possible to use the input originating from the correction system 5 for further training (teaching) of the complementary recognition 3 in a continuous process. Thus, the additional pattern adaptation or training process is affected by the inputs from a correction system, the characters corrected and identified in the correction system being used to initiate a continuous adaptation process, i.e. in this case the characters identified in the correction system are not output as the final result, but passed to the pattern adaptation system (cf. arrow 8 in figure 1). Hence, the pattern adaptation process takes place on-line during the actual recognition process and the required correction work in the correction systems. Before the training process each character is given manually what is known as a "master-identification", which is produced on-line in the correction station. This identification is needed by the training process to be able to calculate the classifier and in the following test the rate of recognition, i.e. rejects, substitutions and hits. This shows a major advantage of direct transfer of training data from the correction station, as this data has already been read by the appropriate scanner and given an identification, so that it is representative. Hence, the manual process of provision of a "master-identification" is made in the correction station itself. This has the advantage over a separate (off-line) training process, since it is less time consuming and cheaper. The proposed on-line pattern adaptation process starts automatically if the following conditions are met: 0 An adequate quantity of classified characters have been collected 0 A specific time for triggering the process is defined (e.g. at a change of shift of employees). The pattern adaptation process generates the classifiers on the basis of classified characters. After this, the recognition result achieved by the newly generated classifiers is again verified in a test run. If the result reaches a certain specified quality, the old complementary recognition is replaced by the current new one. Therefore, as a result of this on-line pattern adaptation process, the subsequent complementary recognition 3 is able to concentrate upon the weaknesses of the primary recognition 1 and again increase the confidence figure of rejected characters. A reject is thereby converted into a recognised character (hit). The on-line continuous collection of these poorly recognised characters and further processing in a training or pattern adaptation process results in a reduction in cost for expenditure incurred on corrections and an increase in the recognition quality. We give below a more detailed explanation in general of the process of pattern adaptation (training process) using figure 2 as an example. The primary recognition 1 has a classifier which, together with the recognition algorithms form a recognition unit. Thus, the recognition performance of this primary recognition is specified and can only be corrected via a software update. But as already indicated above, updates of the classifiers are rarely provided for cost reasons. Therefore, there is only a slight possibility of adaptation to the text, for example of a customer and the recognition weaknesses of certain characters have to be frequently accepted by the user over a long period. However, with the complementary recognition 3, the classifiers are separated from the individual algorithms, since these are calculated with the newly classified characters from the correction station always with the same training algorithms. Consequently, classifiers are continuously improved through the pattern adaptation process, whereby the new classifiers and the existing recognition algorithms produce the new complementary recognition each time. The classifiers can be automatically substituted on-line. The complementary recognition system concentrates primarily upon the weaknesses of the primary recognition 1, with the classifiers also being trained appropriately. In the example in figure 2, a character rejected by the primary recognition 1 (reject) is passed to the complementary recognition 3. The confidence figure for the character "four" is "29", which results in the character being passed to the complementary recognition 3 as it comes below the prescribed threshold value (which in this example is "30"). The first attempt at classification by the complementary recognition 3 also results in rejection of the character, since the confidence figure which would result in classification of the character as "four" is too close to the confidence figure for "nine" (it should be pointed out that both the individual thresholds and the range within which two characters are rejected due to confidence figures being too close to each other, can be specified as desired). Consequently, the complementary recognition 3 also decides to reject the character. The field in which the rejected character lies, i.e. in the present case the segmented character (NCI), which according to a standard (scale) of the character frame can be passed direct to the pattern adaptation process, is now passed on to the correction system 5. Here the character is given a master identification (required identification). Thus, the person at the control system has characterised the character clearly (in this case as "nine"). It is now stored as an image with additional attributes in a database containing candidates for the additional pattern adaptation or training process. Examples of these additional attributes could be: 0 Master identification 0 Scanner resolution 0 Width and height of the character frame 0 Field for the recognition-ID (Reco-ID). The on-line training process (retraining) now ensures that the further classification of the complementary recognition 3 results in an adequately high confidence figure for classification as "nine" (cf. below). Thus, the character is no longer passed on to the correction system, but recognised correctly. The classifiers of the complementary recognition 3 adjust the text on-line (training takes place at the actual work place) and thus increase the recognition quality of the overall system. The classified character from the database of rejected characters is given a form category in the relevant character category. For each character category there are corresponding form categories, to cover the variance of characters for the training process. Fig. 3 shows the principle of the form categories for the character category "four". For each form category within a character category the relevant form categories from other character categories are used to calculate the classifiers automatically in a training run. For preference one uses a process in which zonal characteristics and anti-characteristics are used for recognition (cf. EP-B-O 632 404). This process has the advantage that after classification of the character in the correction system, no further manual attention is necessary in the training process (pattern adaptation process). It is possible to implement the pattern adaptation process in stages. In the first stage the corrected characters from the correction system are stored for a specified period. This data is then processed in the training process, it being possible to carry out the training run at premises of the manufacturer of the recognition software. As a result of the training run, classifiers are obtained which are loaded into the complementary recognition. The complementary recognition now works as a subsequent recognition unit and passes the rejections to the correction system. However, in this first stage the continuous process for initiation of the first recognition system takes place off-line. Here the correction station is only used to collect the initial training data. The characters are classified and the character outline is stored. Processing of the data (the training process) takes place off-line. The classifiers from the first training process are then only loaded once the overall system is put into operation, e.g. at the customer's premises. In the second phase the training process is then incorporated into the complementary recognition, so that it is then initiated on-line and is able to read its data from a character database which is constructed by the correction system. A major advantage of the system proposed here is that it constantly provides better results than the primary recognition system. Even if a new primary recognition system with new superior results were to come onto the market, it would still provide better results with the system described here. It would then only come into operation if a reject occurs, i.e. when improvement is possible via the complementary recognition. In this way it is possible to provide a self-teaching recognition system, which significantly increases the recognition rate compared with the existing systems.

Claims: Apparatus for recognizing a pattern comprising: a first pattern recognition system for recognizing a pattern with a first level of confidence; a complementary pattern recognition system for recognizing with a second level of confidence a pattern that cannot be recognized by the first pattern recognition system with the first level of confidence; a correction system for receiving manual pattern identification input when a pattern cannot be recognized by the complementary pattern recognition system with the second level of confidence; a pattern adaptation system connected to the correction system for receiving pattern identification information from the correction system, the pattern adaptation system including: a database for receiving information originating from input to the correction system, the database including: means for storing patterns of characters to be recognized by the complementary recognition system; and means for storing attribute information for each pattern to be recognized by the complementary recognition system, the means for storing attribute information including: means for storing a master identification of a related stored pattern; means for storing scanner resolution of a related stored pattern; means for storing width and height of a character frame of a related stored pattern; and means for storing a recognition-ID of a related stored pattern; the pattern adaptation system providing adaptation of the pattern recognition information supplied to the complementary recognition system to improve the recognition level of confidence of the complementary recognition system. Apparatus according to claim 1, characterized by the fact that the corrected characters have a special identification. Apparatus according to claim 1 or 2, characterized by the fact that the second recognition system utilises a stock of characters complementary to the first recognition system. Method of recognizing a pattern comprising the acts of: recognizing a pattern in a primary recognition system with a first level of confidence; recognizing in a complementary recognition system with a second level of confidence a pattern that cannot be recognized by the primary pattern recognition system with the first level of confidence; receiving manual pattern identification input at a correction system when a pattern cannot be recognized by the complementary pattern recognition system with the second level of confidence; receiving pattern identification information at a pattern adaptation system from the correction system; storing information originating from input to the correction system in a database including patterns of characters to be recognized by the complementary recognition system and for each pattern to be recognized by the complementary recognition system: storing in the database a master identification of a related stored pattern; storing in the database scanner resolution of a related stored pattern; storing in the database width and height of a character frame of a related stored pattern; and storing in the database a recognition-ID of a related stored pattern; and then adapting the pattern recognition information supplied to then complementary recognition system to improve the recognition level of confidence of the complementary recognition system. Process according to claim 4, characterized by the fact that the pattern adaptation process is performed online. Process according to claim 4 or 5, characterized by the fact that the pattern adaptation process is performed in two stages. Process according to one of claims 4 to 6, characterized by the fact that the pattern adaptation process results in a clear classification of the chraracter incorrectly identified in the second recognition system.

Inventors: MAIER UDO DIPL-ING, RUPPERT WERNER DIPL-ING, MAIER, UDO, DIPL.-ING., RUPPERT, WERNER, DIPL.-ING.
Applicants: IBM, INTERNATIONAL BUSINESS MACHINES CORPORATION
Application year: 1999
Main classification: G06K9
IPC codes: G06K9, G06T7