Patent Application
20130163881
A variety of items used in commerce include machine-readable markings that provide information about the items. The information may not only be used to determine a price of the item (e.g., when checking out at a supermarket) as in some cases, but can also be used to determine a production/usage history of an item (e.g., lot number, date of manufacture, and period of use). Items are often labeled with removable tags, or containers that hold the items can be labeled. In some instances, the markings are physically part of the item. For example, an automotive part can be directly marked through dot peening, etching, hammering, molding, casting, and the like.
Items that are marked may be exposed to conditions that are capable of damaging the markings. For example, a machine part may be used and then refurbished for a second lifetime of use. However, during use of the machine part or during the refurbishing process, the markings may become scratched, worn, soiled, or otherwise rendered more difficult to read. In some cases, if any one of the characters is unreadable for an individual or scanner, the part may undergo more extensive analysis to identify the part or it may even be scrapped completely.
In accordance with various embodiments, systems, methods, and non-transitory computer readable media are provided that are configured to use a sparse distributed memory (SDM) module to identify a character-of-interest. The SDM module includes hard locations that may have stored vector location addresses and stored content counters. The location addresses may be distributed within an address space. For example, the location addresses may have a binomial distribution within an address space or the location addresses may have a non-binomial distribution in the address space. In some embodiments, the location addresses are unevenly distributed in the address space such that that the location addresses are grouped or concentrated together.
In accordance with various embodiments, a system for identifying characters-of-interest from markings on a surface of an object is provided. The system includes a vector-generating module configured to receive and analyze an image of the markings that include the characters-of-interest. The vector-generating module converts at least one of the characters-of-interest into a corresponding feature vector. The feature vector has a vector address. The system also includes a sparse distributed memory (SDM) module. The SDM module includes hard locations having stored vector location addresses within an address space and stored content counters. The location addresses form multiple concentrated groups within the address space. The concentrated groups are associated with different characters of an identification system. The system also includes an identification module that is configured to identify the character(s)-of-interest using the SDM module. The identification module is configured to determine a relative distance between the vector address of the feature vector and the location addresses of the hard locations. The hard locations that are within a predetermined relative distance from the vector address are activated locations. The identification module is also configured to provide a suggested identity of the corresponding characters-of-interest that is based upon the stored content counters of the activated locations.
In accordance with other various embodiments, a non-transitory computer readable medium for identifying characters using at least one processor and a sparse distributed memory (SDM) module is provided. The computer readable medium includes instructions to command the processor to receive image data relating to an object having a surface with markings thereon. The markings include characters-of-interest. The processor is also commanded to analyze the image data to convert at least one of the characters-of-interest in the image data into a corresponding feature vector. The feature vector has a vector address. The processor is also commanded to identify said at least one of the characters-of-interest using the SDM module. The SDM module includes hard locations having stored vector location addresses within an address space and stored content counters. The location addresses form multiple concentrated groups within the address space. The concentrated groups are associated with different characters of an identification system. The identifying operation includes determining a relative distance between the vector address of the feature vector and the location addresses of the hard locations. The hard locations that are within a predetermined relative distance from the vector address are activated locations. The identifying operation also includes providing a suggested identity of the corresponding characters-of-interest that is based upon the stored content counters of the activated locations.
In accordance with yet other various embodiments, a method of identifying a character-of-interest is provided. The method includes receiving image data relating to an object having a surface with markings thereon. The markings include characters-of-interest. The method also includes analyzing the image data to convert at least one of the characters-of-interest in the image data into a corresponding feature vector. The feature vector has a vector address. The method also includes identifying said at least one of the characters-of-interest using an SDM module. The SDM module includes hard locations having stored vector location addresses within an address space and stored content counters. The location addresses form multiple concentrated groups within the address space. The concentrated groups are associated with different characters of an identification system. The identifying operation includes determining a relative distance between the vector address of the feature vector and the location addresses of the hard locations. The hard locations that are within a predetermined relative distance from the vector address are activated locations. The identifying operation also includes providing a suggested identity of the corresponding characters-of-interest that is based upon the stored content counters of the activated locations.
In accordance with yet other various embodiments, a method of providing a sparse distributed memory (SDM) module using a processor. The method includes obtaining first and second weighting vectors having different character addresses. The first and second weighting vectors are based upon different characters of an identification system. The method also includes generating first noise addresses and second noise addresses within a common address space. The first noise addresses are within a first group distance from the character address of the first weighting vector. The second noise addresses are within a second group distance from the character address of the second weighting vector. The method also includes storing hard locations in the SDM module. At least some of the hard locations have the first noise addresses and at least some of the hard locations have the second noise addresses.
The following detailed description of certain embodiments will be better understood when read in conjunction with the appended drawings. To the extent that the Figures illustrate diagrams of functional blocks of various embodiments, the functional blocks are not necessarily indicative of the division between hardware. Thus, for example, one or more of the functional blocks may be implemented in a single piece of hardware or multiple pieces of hardware. It should be understood that the various embodiments are not limited to the arrangements and instrumentality shown in the drawings. Additionally, the system blocks in the various Figures or the steps of the methods may be rearranged or reconfigured.
As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to “one embodiment” or “an exemplary embodiment” and the like are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments “comprising” or “having” an element or a plurality of elements having a particular property may include additional such elements not having that property.
In various embodiments, the markings are configured to identify and/or provide information about the object that has the markings. For example, the markings can be used to determine any information about the manufacture or subsequent use of the object, such as a date of production, the particular manufacturer or plant that made the object, composition of the material(s) used, when and by whom the object was inspected, when and how long the object was used, the machine that used the object, and the like.
Embodiments described herein may be used to analyze imaged markings on an object. The imaged markings may include characters or symbols that are part of an identification system. In particular embodiments, the markings are physical markings. As used herein, “physical markings” include markings that are at least partially made by morphological changes along the surface of the object. As opposed to two-dimensional markings made on paper, physical markings may be three-dimensional. For example, objects described herein may have physical markings that were formed by changing a relatively smooth surface through dot peening, etching, hammering, scratching, stamping, impressing, and the like. Physical markings may also be made through molding, casting, and the like in which, for example, a material is poured into a mold and allowed to cure, set, or otherwise harden. The above examples are not intended to be limiting and, as such, physical markings could be made through other processes. Moreover, the use of the term “markings” is not intended to be limited to a particular language or identification system. For example, the markings may include letters of a recognizable language and numbers (i.e., alphanumeric characters). The markings may also include other recognizable symbols (e.g., Greek symbols) or symbols specifically designed for a particular identification system. An exemplary identification system may include the letters of the English alphabet (A, B, C . . . ) and/or the numbers 0-9.
An object may include more than one type or style of marking. For example, at least one of the physical markings may be cast or molded by the manufacturer while other physical markings may be dot peened by the vendor or user of the object. Markings may be located on a planer surface or a surface that has some curvature or contour.
Although embodiments described herein are described with particular reference to physical markings, other embodiments may be used to image and analyze two-dimensional markings. For example, the imaged markings could be made by applying ink or paint to cardboard containers, envelopes, paper, or other substantially planar surfaces. As such, when the terms “markings,” “characters,” or “characters-of-interest” are not modified by “physical,” the term includes two-dimensional markings, characters, symbols, and the like.
The objects imaged may be any item capable of having the markings made thereon. In particular embodiments, the object is a mechanical item configured to be used in a machine or other industrial application in which the object has physical markings on surface(s) of the object. For example, the objects could be automotive parts or aircraft parts (e.g., rotors). The objects can be large and heavy such that the objects must be scanned one at a time by an individual. The objects can also be small, such as items used with handheld electronic devices. The objects can also be found or used in environments that increase the likelihood of the physical markings being damaged or dirtied.
With respect to
In the illustrated embodiment, the system 100 may include a single housing 102 that is configured to hold the imager 104 and other components of the system 100. The housing 102 may be sized and shaped for an individual to hold and carry and may include a grip 103. In such embodiments, the system 100 may resemble a handheld price scanner. Alternatively, the system 100 and the housing 102 are not sized for an individual to carry. By way of one example only, the system 100 could be a part of an assembly line or other automated system in which the objects are imaged as the objects pass by on a conveyor belt. The alternative system 100 could have a stationary position with respect to the conveyor belt.
The imager 104 may include a lens or lenses 124 and an imaging sensor 126 configured to acquire the images 106. The imaging sensor can be a charge-coupled device (CCD), a complimentary metal oxide semiconductor (CMOS), or another type of imaging sensor. The imager 104 may include other features that may be used with imagers/cameras, such as an auto-focus mechanism, viewfinder, and/or a lighting system that is configured to illuminate the surface 109 of the object 108 during acquisition of the image 106. As shown, the system 100 may also include a user interface 122 that may receive user inputs from the user and/or communicate information to the user. For instance, the user interface 122 may include a display that identifies the objects scanned by the system 100 or provides suggested identities of the characters-of-interest and/or the objects. As used herein, providing a “suggested identity” and like terms may include providing an ideal character (e.g., the letter or number without any noise) or may include providing a closer estimation of how the character-of-interest should appear, which may include some noise. In some embodiments, the user can enter information or instructions to assist the system 100 in identifying the characters-of-interest.
As shown in
The modules 116-121 include a segmentation module 116, an identification module 117, a sparse distributed memory (SDM) module 118, an interface module 119, a database 120, and a noise-generating module 121. The segmentation module 116 is configured to analyze the image 106 and convert at least a portion of the markings 110, 111 into a corresponding feature vector (described in greater detail below). For example, in some embodiments, the segmentation module 116 separates the image into portions to isolate characters-of-interest. The portions of the image 106 may then be converted into pixel (e.g., binary) images and analyzed to generate corresponding feature vectors. The identification module 117 is configured to receive the feature vector and, for at least one of the characters-of-interest, use the SDM module 118 to identify the character(s)-of-interest. The interface module 119 may be configured to communicate with the other modules 116-118, 120 and the user interface 122. The noise-generating module 121 is described in greater detail below.
The physical markings shown in the image may include the characters-of-interest as well as other unwanted physical changes, such as any unintended scratches or unwanted dirt that may have collected onto the surface. For example, the enhanced image 130 shown in
The segmentation module 116 can be configured to separate the enhanced image 130 to isolate the characters-of-interest. In some embodiments, the segmentation module 116 may separate the image 130 into line sections 135-137, respectively, as shown in
The segmentation module 116 may also be configured to convert the character images 141-143 into the binary images 151-153. In some embodiments, the segmentation module 116 scans along the X and Y-axes of each of the character images 141-143 to determine dimensions of the character-of-interest within the corresponding character image and remove extraneous portions. For example, the segmentation module 116 may determine a height H and width W of the character-of-interest in the character image 143 (i.e., the number zero) and remove outer portions of the character image 143 that surround the character-of-interest thereby providing an edge-enhanced character image (not shown).
The segmentation module 116 may then analyze separate blocks or cells of the edge-enhanced character image to pixelize (or binarize) the image. By way of example only, the segmentation module 116 may compare an intensity signal of each of the cells to a predetermined threshold. If the intensity signal of the corresponding cell exceeds the threshold, the cell is labeled as having a first value (e.g., 1). If the intensity signal of the corresponding cell is less than the threshold, the cell is labeled as having a different second value (e.g., 0). If the intensity signal is equal to the threshold, the cell can be labeled as having the first or second value. As shown in
Although the pixelization process described above labels each cell as having only one of two cell values, other pixelizing processes may use a different number of cell values (e.g., 1 of N values). For example, there can be three possible cell values depending upon the intensity signal of the cell. Moreover, the pixelization process can be modified in other manners if desired. For example, instead of only considering the intensity signal of the corresponding cell, the intensity signals of adjacent cells may be considered when assigning a cell value.
Each cell 162 has a cell location that can be defined by the row and column of the cell 162. As shown, row A includes cells that exceeded the predetermined threshold as discussed above (indicated in black) and also cells that did not exceed the threshold (indicated in white). Specifically, cell A1 has a cell value of 0; cell A2 has a cell value of 1; cell A3 has a cell value of 1; cell A4 has a cell value of 1; and cell A5 has a cell value of 0. Rows B-G have cells 162 with cell values as indicated. As shown in the feature vector 161, the string of cell values in the feature vector 161 is based on the cell locations and the values of the cells. As such, the cell values may be referred to as coordinates in the feature vector and the series of coordinates may constitute a vector address 165 of the feature vector 161. For example, the first three coordinates in the feature vector 161 (or the vector address 165) have values that are derived from the cells A1, A2, A3 . . . and the last three coordinates have values that are derived from the cells G3, G4, G5. Thus, the vector address 165 is at least partially defined by (a) the cell locations and (b) the values of the cells.
In some SDM algorithms used in various embodiments, the feature vector (or input vector) includes the vector address 165 and a data pattern. For example, the vector address 165 of the feature vector 161 is: 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0. In an exemplary embodiment, the vector address 165 and the data pattern of the feature vector 161 are the same (i.e., the series of coordinates may also be the data pattern). This may be referred to as the autoassociative mode. Although the illustrated embodiment uses an autoassociative protocol, other embodiments may have different values representing the vector address and the data pattern. For example, the vector address may be a three-digit number and the data pattern may be the same as shown in
As shown in
In some embodiments, the system 100 is configured to train the SDM module 118. Alternatively, a previously trained or modified SDM module 118 may be provided to the system 100. To train the SDM module 118, the system 100 may scan numerous objects (e.g., tens or hundreds) having markings thereon and convert the markings into feature vectors as described above. The feature vectors may then be written or stored into the SDM module 118 to train the SDM module 118 in identifying characters-of-interest. More specifically, the identification module 117 may determine a relative distance between the vector address 165 of the feature vector 161 and at least some of the stored vector location addresses 174 of the hard locations 172. In some embodiments, the identification module 117 determines a relative distance between the vector address 165 and each one of the stored vector location addresses 174 of the hard locations 172. By way of illustration, the vector address of the feature vector to-be-stored, such as the feature vector 161, is represented in the address space 170 as cross-hairs 185. As shown, the cross-hairs 185 are located relatively close to the hard locations 172 that are indicated as numbers (nos.) 1, 3, 4, and 6 in the address space 170.
The identification module 117 may determine the relative distance between the feature vector to-be-stored and the hard locations 172 using various algorithms. The relative distance may be determined by calculating, for example, the Hamming distance between the feature vector to-be-stored and one of the hard locations 172. Other exemplary algorithms for calculating the relative distances include the Manhattan distance and the Euclidean distance. The Hamming distance essentially determines the number of coordinates in the feature vector to-be-stored that have a different value than the corresponding coordinates 175 of the stored location address of each of the hard locations 172. Using the hard locations 172 as an example of calculating the Hamming distance, the Hamming distance between the hard location no. 1 and hard location no. 2 is 1 unit, because the hard locations nos. 1 and 2 only differ at the second coordinate 175B. The relative distance between the hard location no. 1 and the hard location no. 3 is 4 units, because the hard locations nos. 1 and 3 differ at all four coordinates 175A-175D.
The identification module 117 may then compare the calculated relative distance associated with the hard locations 172 to a predetermined distance or threshold value. The circle 180 that surrounds the hard locations nos. 1, 3, 4, and 6 in
After identifying the activated locations from the hard locations 172, the data pattern of the queried feature vector can then be stored into the stored content counters 178 for those hard locations 172 that are activated. For example, the stored content counters 178 may be incremented and/or decremented. Each counter 178A-178D corresponds to one of the coordinates (e.g., the counter 178A corresponds to the coordinate 175A, the counter 178B corresponds to the coordinate 175B, and so on). According to one embodiment, for each coordinate in the feature vector to-be-stored that has a value of 1, the corresponding counter 178 increases by 1 (or incremented by 1). For each coordinate having a value of 0, the corresponding counter 178 decreases by 1 (or decremented by 1). By way of one particular example, if the data pattern of the feature vector to-be-stored was <1, 0, 1, 0>, then—for each one of the activated locations—the first counter 178A would add one to its total; the second counter 178B would subtract one from its total; the third counter 178C would add one to its total; and the fourth counter 178D would subtract one from its total. After numerous feature vectors have been stored into the SDM module 118, the stored content counters 178 of the hard locations nos. 1-7 might be as shown in
The identification module 117 is also configured to provide a suggested identity of the character(s)-of-interest. In other words, the identification module 117 may identify (or provide a better estimation of) the character-of-interest to the user of the system 100. In some embodiments, the identification module 117 may use a feature vector to retrieve an output vector that is then used to provide the suggested identity. The identification operation is similar to the training operation discussed above. However, the SDM module 118 is typically already trained or modified in some manner before the system 100 is used to analyze markings on objects.
Again, the feature vector being applied to the SDM module 118 during the identification operation may be represented as the cross-hairs 185 in the address space 170. The location of the cross-hairs 185 is based on the vector address of the feature vector, which can be determined by the segmentation module 116. As before, the identification module 117 may determine the relative distance between the feature vector and the hard locations 172 (e.g., by calculating the Hamming distance or through other algorithms, such as Manhattan or Euclidean). The identification module 117 then compares the calculated relative distances to a predetermined distance value to identify the activated locations. Again, the hard locations nos. 1, 3, 4, and 6 in
In some embodiments, to provide the suggested identity of the character-of-interest, the stored content counters 178 of the activated locations are then summed-and-thresholded as indicated at reference numeral 184. More specifically, the counters 178A of the activated locations are combined (e.g., summed or added together); the counters 178B of the activated locations are combined; the counters 178C of the activated locations are combined; and the counters 178D of the activated locations are combined. The resulting vector may be referred to as a summed-content vector 187 that includes a set of values. As shown in
According to one embodiment, each of the sums is then compared to a threshold to provide an output vector 186. For example, if the value in the summed-content vector 187 is greater than a threshold value of zero, a value of 1 is provided in the corresponding output vector 186. If the value in the summed-content vector 187 is less than a threshold value of zero, a value of 0 is provided in the corresponding output vector 186. If the value is equal to zero, values of 0 or 1 can be provided in the corresponding output vector 186. In the illustrated example, the output vector 186 has values of <1, 1, 0, 1>. In other embodiments, a threshold value other than zero may be used.
Accordingly, in some embodiments, the feature vector in the training and retrieval operations may facilitate identifying a select number of the hard locations 172 as activated locations in the address space 170 of the SDM module 118. The stored content counters of the activated locations may then be used to generate a single output vector 186. The output vector 186 is based on the stored content counters of the activated locations that, in turn, can be based on previously stored feature vectors.
In some embodiments, the output vector 186 may be converted into a binary image, such as the binary image 160, using a similar cell location and value standard as described above. More specifically, each coordinate in the output vector 186 having a value of 1 will correspond to a cell that is black and each coordinate having a value of 0 will correspond to a cell that is white. The binary image 160 may then be provided to the user through, for example, the user interface 122. In such embodiments, the suggested identity may include some noise. In other embodiments, the output vector 186 may undergo further analysis or processing to determine an ideal character that the output vector is closest to that does not include noise. The ideal character may then be provided to the user interface 122. Accordingly, the suggested identity shown to the user may or may not correspond to the ideal character.
Before or after the imaging operation at 202, the SDM can be trained or provided at 204. In one example, the SDM can be trained using the images acquired by the system as described above with respect to
In other embodiments, the training operation at 204 may constitute receiving an SDM. More specifically, the training operation at 204 may include receiving an SDM that has been previously trained or modified in some manner. For example, the stored content counters of the hard locations in the trained SDM may already have particular values. As such, the time necessary to train the SDM by repeatedly storing feature vectors may be avoided. The SDM may be configured for a particular object. By way of one particular example only, an SDM may be trained for rotors that were manufactured in a particular year at one plant. It should be noted that the training operation at 204 may or may not be performed.
The method 200 may also include receiving at 206 image data that relates to the object having characters-of-interest. The image data may be received from the imager or other camera system immediately after acquisition of the image, or the image data can be received from a database. After receiving the image data, the image data may be analyzed at 208 to convert at least one of the characters-of-interest into a corresponding feature vector as described above.
With the SDM, the method 200 includes identifying at 208 activated locations by determining a relative distance between the feature vectors and the stored vector location addresses of the hard locations. As described above, non-limiting examples of the relative distance include the Hamming distance, the Manhattan distance, or the Euclidean distance. Regardless of the manner in calculating the relative distance, the activated locations may be determined by comparing the calculated relative distances to a predetermined distance value.
The method 200 also includes providing a suggested identity at 210 of the character(s)-of-interest based on the stored content counters of the activated locations as described above. The suggested identity may be generated from an output vector provided by the SDM. The suggested identity may be a character that includes some noise or an ideal character without any noise.
The character address 411 includes the same set of coordinates as the vector address 165 of the feature vector 161 (i.e., the character address 411 is based on a standard representation of the character zero). In particular embodiments, the noise addresses 402-406 are derived from the character address 411 of the weighting vector 401. To provide the noise addresses 402-406, the noise-generating module 121 (
In the illustrated example, the noise address 402 has different values than the character address 411 at coordinate positions 3, 5, 7, 10, 23, and 30. The noise address 403 has different values than the character address 411 at coordinate positions 5, 10, 12, 19, 21, and 35. In these examples, the noise-generating module 121 introduced noise to the character address 411 by changing 20% (⅕) of the coordinates. In the illustrated embodiment, the noise-generating module 121 provided noise addresses with at most 20% noise with respect to the character address of the weighting vector. However, in other embodiments, the noise-generating module 121 may provide noise addresses with less than 20% noise or more than 20% noise (e.g., 5%, 10%, 25%, 30% noise). Moreover, in an exemplary embodiment, the noise is randomly generated. In other embodiments, the noise-generation may be more controlled or less random. For example, the letter “O” may be changed in a different manner than the number “1” to provide the noise addresses.
In other words, the noise-generating module 121 may make changes to a character address that correlates to a clear character representation (e.g., the standard zero or letter A for the particular identification system) thereby providing addresses that correlate to character representations that are not as clear, but may still be identifiable as the character. For illustrative purposes, the output representation 194 (
Hard locations having the noise addresses 402-406 may then be stored into an SDM module. More specifically, the noise addresses 402-406 may become the stored vector location addresses described above. Although only five noise addresses 402-406 are shown
Because the noise addresses differ from the associated character address by at most a predetermined percentage or fraction, the noise addresses (or corresponding hard locations) may form concentrated groups in the address space. More specifically, the noise addresses of one group may be within a predetermined group distance from the character address. The group distance is the same as the relative distance described above, but has been referenced differently for clarity. The group distance may be, for example, the Hamming distance between a noise address and the associated character address. For example, the noise address 402 is 6 units away from the character address 411, because the noise and character addresses 402, 411 differ at 6 positions. The coordinates of the noise address 402 that differ with the corresponding coordinates of the character address 411 are underlined. The noise address 405 is 5 units away from the character address 411 because the noise and character addresses 405, 411 only differ at 5 positions. Thus, for each of the noise addresses 402-406, the group distance between the noise address and the character address 411 is at most 7 units. As such, the noise addresses (and corresponding hard locations) may be described as being clustered around the character address 411. The SDM module may have multiple concentrated groups in which each concentrated group is clustered around a different character address.
In other embodiments, the noise addresses may be derived from actual acquired images that include characters-of-interest. At least some characters-of-interest in the acquired images may not be ideal. For example, the characters may have been improperly written or subsequently scratched and/or dirtied. In such embodiments, the segmentation module 116 may provide a feature vector for each character-of-interest as described above. The corresponding vector address may then be used as a noise address. A hard location having the noise address may then be stored in the SDM module. Also, in embodiments that include concentrated groups of hard locations, the hard locations may include stored content counters with predetermined numbers.
As an illustrative example,
As shown in
When the SDM module 118 is modified to include such concentrated groups of hard locations, the SDM module 118 may be weighted or biased to identify characters of an identification system. As described above, the identification module 117 may calculate the relative distance between the vector address of the feature vector and the hard locations to determine or identify the activated locations. By way of one example, a feature vector may have a vector address that is indicated by the cross-hairs 435. After determining the relative distance between the vector address and the location addresses of the hard locations, the identification module 117 may compare the calculated relative distances to a predetermined value or radius as described. This predetermined value is represented by the circle 436. As shown, the activated locations are the hard locations 412 that are within the circle 436. As shown, a greater number of the activated locations exist closer to the character address or base location 428 due to the concentrated group 418. The resulting summed-content vector will be biased by these activated locations. By comparison, the distribution of the activated locations within the circle 180 (
A plurality of noise addresses based on the weighting vectors may be generated at 454. For example, first noise addresses and second noise addresses may be generated at 454 that are derived from the character addresses of the first and second weighting vectors. The first noise addresses may be within a predetermined group distance from the character address of the first weighting vector. The second noise addresses may be within a predetermined group distance from the character address of the second weighting vector. As such, the generating operation 454 may also be described as generating concentrated groups of noise addresses. The method 450 also includes storing at 456 the first and second noise addresses as location addresses of hard locations in the SDM module thereby providing concentrated groups of hard locations.
At least one technical effect of some embodiments is the suggested identity of a character-of-interest using an SDM module in which the character-of-interest has been damaged, dirtied, or are otherwise rendered more difficult to read. Other technical effects for some embodiments include training and/or modifying an SDM module for a system or device that is subsequently used to provide suggested identities of the characters-of-interest. Other technical effects for some embodiments include SDM modules having concentrated groups of hard locations that may provide more accurate suggested identities when markings on an object are scanned.
The various components and modules described herein may also be implemented as part of one or more computers or processors. The computer or processor may include a computing device, an input device, a display unit and an interface, for example, for accessing the Internet. The computer or processor may include a microprocessor. The microprocessor may be connected to a communication bus. The computer or processor may also include a memory. The memory may include Random Access Memory (RAM) and Read Only Memory (ROM). The computer or processor further may include a storage device, which may be a hard disk drive or a removable storage drive such as an optical disk drive, solid state disk drive (e.g., flash RAM), and the like. The storage device may also be other similar means for loading computer programs or other instructions into the computer or processor.
As used herein, the term “computer” or “module” may include any processor-based or microprocessor-based system including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), graphical processing units (GPUs), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are exemplary only, and are thus not intended to limit in any way the definition and/or meaning of the term “computer” or “module”.
The computer or processor executes a set of instructions that are stored in one or more storage elements, in order to process input data. The storage elements may also store data or other information as desired or needed. The storage element may be in the form of an information source or a physical memory element within a processing machine.
The set of instructions may include various commands that instruct the computer or processor as a processing machine to perform specific operations such as the methods and processes of the various embodiments. The set of instructions may be in the form of a software program, which may form part of a tangible, non-transitory computer readable medium or media. The software may be in various forms such as system software or application software. Further, the software may be in the form of a collection of separate programs or modules, a program module within a larger program or a portion of a program module. The software also may include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to operator commands, or in response to results of previous processing, or in response to a request made by another processing machine.
As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a computer, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program.
It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the various embodiments without departing from their scope. While the dimensions and types of materials described herein are intended to define the parameters of the various embodiments, the embodiments are by no means limiting and are exemplary embodiments. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the various embodiments should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means-plus-function format and are not intended to be interpreted based on 35 U.S.C. §112, sixth paragraph, unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function void of further structure.
This written description uses examples to disclose the various embodiments, including the best mode, and also to enable any person skilled in the art to practice the various embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the various embodiments is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if the examples have structural elements that do not differ from the literal language of the claims, or if the examples include equivalent structural elements with insubstantial differences from the literal languages of the claims.
