Image processors

Abstract: Dominant typeface, including point size, of text in an image is identified by measuring distances across connected components in each of plural directions to obtain distribution data. The distribution data indicate a distribution of distances across connected components in each direction for the image. The distribution data are then used to obtain typeface data indicating the dominant typeface of characters in the text. Distance can be measured from each of a set of pixels that are inside or on edges of connected components to an edge of a connected component. The distances for each direction can be combined, such as by obtaining an average or other measure of central value, so that the combined distances indicate the distribution of distances in the direction. The distribution data can be used to form a profile that can be compared or clustered with other profiles to identify similar profiles or to find a matching profile. Skew can be obtained and used in forming profiles that are independent of skew. The comparisons can be independent of differences in character scale. A machine can store a set of model profiles of known typeface and point size in memory and can compare a profile for an image with each model profile to find the closest match, providing an identifier of its dominant typeface and point size. A machine could also store an input image in memory, obtain data indicating its dominant typeface, modify the stored data according to the dominant typeface, and provide an output image based on the modification. Profiles can be stored or transmitted, and a product can include a model profile together with code for operating a processor to obtain a profile for an image and code for operating the processor to compare the profile for the image with the model profile.

Description: The present invention relates to image processors that obtain information about the dominant typeface of text in an image. US-A-3,634,822 describes character recognition techniques that identify characters in each of three different fonts. The present invention provides techniques that obtain data indicating a distribution of distances across connected components in each of a plurality of directions within an image that includes text. The distribution data are then used to obtain typeface data indicating a dominant typeface for characters in the text. The measurment of distances accross components is shown in figure 10a and b of the publication Pattern Recognition, vol.23, no.11, 1990, p.1141-1154, by Akiyama et. al., titled "Automated Enty System for Printed Documents". One aspect of the invention is based on the discovery that the dominant typeface of characters in an image can be identified without recognizing characters. This discovery avoids problems that arise in identifying typeface based on character recognition, whether by template matching or feature extraction and analysis. In order to perform character recognition, characters must be segmented, but segmenting may be difficult if characters have bled together or are connected. In addition, errors in recognizing characters may cause errors in identifying typeface. This invention is further based on the discovery of techniques that can identify typeface without segmenting or recognizing characters and without extracting information specific to a single character. The techniques produce, for each of a number of directions, distribution data indicating a distribution of distances across connected components in the direction for the image. Surprisingly, the distribution data can then be used to obtain typeface data indicating a dominant typeface of the characters. The distance distributions include information about character shape that indicates dominant typeface. For example, the distance distributions may indicate lengths, widths, and orientations of strokes in characters, including such typeface related features as serif size and shape, stroke width variability, vertical component strength, aspect ratio, rhythm, and density of characters. The characters of different typefaces may therefore have distinguishable distance distributions at certain directions. This aspect of the invention can be implemented, for example, by obtaining an average distance in each of a number of equally separated directions that together cover a range of 360°, so that the average distances together form a profile of distance distribution as a function of direction for the image. The directions can be separated by a relatively small angle for fine coverage of the range. Profiles can be compared to find similarities and differences. For example, a profile for text of an unknown dominant typeface could be compared with one or more model profiles for known typefaces to determine how well it matches each known typeface; if it matches one of the known typefaces very well, its dominant typeface could be identified as the matching typeface, and an identifier of the typeface could be provided, perhaps as part of character recognition output, or profiles of a number of images of text could be clustered to find equivalence classes. Two profiles for texts of unknown dominant typeface could be compared to determine how well they match. Each of these operations can provide useful information because the profiles indicate dominant typeface. The present invention will now be described by way of example with reference to the accompanying drawings, in which: Fig. 1 is a schematic view of portions of a character in different typefaces showing measurements taken from a similar location; Fig. 2 is a flowchart showing general steps in obtaining data indicating dominant typeface according to the invention; Fig. 3 is a schematic view of part of an image with distance measurements made across a connected component; Fig. 4 is a graph showing distance as a function of direction, from which data indicating dominant typeface can be obtained; Fig. 5 shows how two graphs of distance as a function of direction for different typefaces may be compared; Fig. 6 is a flowchart showing steps in measuring distance from a start location to an end location; Fig. 7 is a flowchart showing steps in storing the distance data obtained in Fig. 6; Fig. 8 is a flowchart showing steps in obtaining an average profile for an image using distance data stored as in Fig. 7; Fig. 9 is a flowchart showing steps in obtaining data identifying dominant typeface; Fig. 10 is a flowchart showing steps in clustering an image profile with other profiles; Fig. 11 is a flowchart showing steps in obtaining the dominant typeface of an image using information about skew; Fig. 12 is a flow chart showing general steps in storing a profile from Fig. 8 on a storage medium; Fig. 13 is a flow chart showing general steps in transmitting a profile from Fig. 8; Fig. 14 is a schematic block diagram of a system in which the invention is implemented on a serial machine; Fig. 15 is a schematic block diagram of a system in which the invention is implemented on a parallel machine; Fig. 16 is a flow chart of steps in producing a profile of distance as a function of direction; Fig. 17 is a flowchart of steps using the average profile to determine the dominant typeface of the text in the image; Fig. 18 is a sample of an image of text in Bookman typeface style; Fig. 19 is a profile of distance as a function of direction for the image of Fig. 18; Fig. 20 is a sample of an image of text of Avant Garde typeface style; Fig. 21 is a profile of distance as a function of direction for the image of Fig. 20; Fig. 22A, 22B, 22C, 22D, and 22E are samples of an image of text of Avant Garde typeface with different degrees of degradation; Fig. 23A, 23B, 23C, 23D, and 23E are samples of an image of text of Helvetica typeface with different degrees of degradation; Fig. 24A, 24B, 24C, 24D, and 24E are samples of an image of text of Bookman typeface with a varying degree of degradation; Fig. 25A, 25B, 25C, 25D, and 25E are samples of an image of text of Bodoni typeface with a varying degree of degradation; Fig. 26A, 25B, 25C, 25D, and 25E are samples of an image of text of Palatino typeface with a varying degree of degradation, and Fig. 27 illustrates an application ofthe invention in a copying machine. Figs. 1 through 5 illustrate general features of the invention. Figs. 1A, 1B, and 1C show measurements taken from a location in a similar portion of a character in different typefaces. Fig. 2 is a flowchart showing general steps in obtaining data indicating dominant typeface. Fig. 3 shows an image with distance measurements made inside a connected component. Fig. 4 shows a profile based on measurements like those in Fig. 3 from which data indicating dominant typeface can be obtained. Fig. 5 shows how the profiles of different typefaces may be compared. Figs. 1A, 1B, and 1C show how different dominant typefaces may be detected. Figs. 1A, 1B, and 1C show the same portion of a text character "T" in different typefaces. Fig. 1A shows typeface Quartz. Fig. 1B shows typeface Helvetica. Fig. 1C shows typeface Classic. Measurements made from a similar position in the character will produce different distances across connected components in different typefaces. For example, points 12, 22, and 32 are each in a similar position in the characters 10, 20, and 30, respectively. The distance from point 12 to point 14 is much longer that the distance from point 22 to point 24, but shorter than the distance from point 32 to 34, even though each measurement is made in a similar direction. Typeface differences like those shown in Figs. 1A, 1B, and 1C may explain why distance measurements can be used to obtain data indicating dominant typeface. Fig. 2 shows general steps in doing so. In the flowchart of Fig. 2, the step in box 36 measures distances across connected components in an image of text in each of a number of directions. The distances are used to obtain distribution data indicating a distribution of distances across connected components in each direction for the image. The distribution data are used by the step in box 38 to obtain typeface data indicating the dominant typeface of characters in the image. Fig. 3 shows connected component 40 in which distance measurements can be made from a starting location to various locations that meet an edge criterion, depending on the direction from the starting location. Starting location 42 is located within connected component 40. From location 42, a first distance d 1 is measured along a line at a first direction to location 44 where the line meets an edge. A second distance, d 2 , is measured along a line at a second direction separated by an angle of  1 from the first direction to location 46 where the line meets an edge. Distance d 3 to location 48 is measured along a line at a third direction separated from the second direction by an angle of  2 . The measurements can continue in this way around starting location 42 to cover a range of 360°. If each  i is sufficiently small, the measurements of distance capture information about details of character shape. For example, the angles of separation can have equal values,  i =(360°)/ n , where n , the number of directions that are measured, is sufficiently large to capture detailed information. Fig. 4 shows a profile of distribution data based on distance measurements as shown in Fig. 3. Measurements made at several locations in the image can be combined to produce profile 60 by obtaining, for each direction shown on the horizontal axis, data indicating the distribution of distance measurements in the direction. The profile of distances from a black pixel across black pixels to the nearest edge pixel in each of a large number of directions is highly informative about typeface. When the distribution data at each direction are used to form a combined profile for an image, the profile indicates typeface features. For example, if the distribution data in each direction are an average of distances measured in that direction, the height of the tallest peak corresponds to the average length of vertical strokes of characters, and small humps on either side of the peak may indicate the presence of serifs. Furthermore, if the characters in the image have a dominant typeface, the profile indicates the dominant typeface. Fig. 5 shows how profiles of different typefaces produce profiles with different characteristics. Profile 60, from Fig. 3, is a profile of text of Bookman typeface. Profile 70 is obtained from a second image of text of Avant Garde typeface. Points in the profiles may be compared to obtain data about the difference of the two profiles. Point 62 on profile 60 indicates the distribution data at a direction of about 200° from the direction of lines of text in the first image. Point 72 on profile 70 indicates the distribution data for the second image in the same direction. The difference between points 62 and 72 can be stored, and combined with information about the difference of other points on the profiles to indicate characteristics of the image such as typeface and point size. The flowcharts of Figs. 6, 7, and 8 show a method that can be used to obtain a profile like that shown in Fig. 4. The flowchart of Fig. 6 shows steps in measuring distance from a starting location to an ending location that meets a criterion. The flowchart of Fig. 7 shows steps in storing distance data from Fig. 6 in a data structure. The flowchart of Fig. 8 shows steps in obtaining an average profile for an image from the distance data of Fig. 7. Fig. 6 shows steps in obtaining data indicating the distance along a line in a direction from a first location to the nearest location along the line meeting a criterion. The step in box 80 starts at the first location. The step in box 81 resets a distance count. The step in box 82 increments the distance count. The test in box 84 determines whether the distance count exceeds a limit. The value of the distance limit could be set to be three or four times the size of the largest expected character. This reduces the influence of non-character connected components, like graphics, that are substantially larger than a character. If the distance limit is not exceeded, the step in box 86 accesses data defining the next location along the line. The step in box 88 then tests whether the accessed location meets the criterion. If the accessed location does not meet the criterion, another iteration begins with the step in box 82. Iterations continue until a location is found that meets the criterion in box 88, or the distance count exceeds the distance limit in box 84. For example, if the criterion is a white pixel, the step of box 88 would test whether the next location is a white pixel, and iterations would continue until a white pixel is found or the distance limit is exceeded. At that point, the step in box 90 returns the distance count, which indicates either the distance to the nearest white pixel or the distance limit. Fig. 7 shows general steps in storing distance data for a starting location in a data structure. The step in box 92 begins an iterative loop that handles each of a number of directions. At each iteration, the step in box 94 obtains a distance count in the next direction, following the steps in Fig. 6. Then the step in box 96 stores the distance count in a data structure in memory so that the count for each direction can be accessed with the direction for which it was obtained. When distances in all directions have been measured, the distance data structure for the location is complete, and may then be either used or stored. Fig. 8 shows steps in combining distance data for a plurality of selected locations in an image to obtain a profile of distance as a function of direction for the image that includes the locations. The step in box 100 begins an iterative loop that handles each of a number of locations. Distance measurements are obtained for the next location in the step in box 102, such as by following the steps in Fig. 7 to produce a distance data structure. The step of box 104 combines the distance measurements with previously combined distance measurements, such as by adding the distances indicated by distance data in each direction. If distance measurements have been obtained and combined for enough locations, the profile of average distance, or "average profile," is obtained in the step in box 106, such as by dividing the added distances in each direction by the number of distance measurements in the direction. The steps in boxes 104 and 106 can be allocated differently. For example, the distance data of all the locations could be obtained and stored, omitting step 104 entirely, and then the step in box 106 could obtain the average profile directly from the distance data of individual locations. Also, the combined profile that results from the last iteration of box 104 could be used directly, without averaging, so that the step in box 106 is optional. The choice of how to implement these steps can depend on efficiency or other practical considerations. Fig. 9 shows steps in obtaining data identifying dominant typeface. The step in box 110 obtains a profile for an image that includes text having a dominant typeface. The profile may be obtained using a technique similar to that shown in Fig. 8 or using another technique that generates a profile indicating the dominant typeface. The step in box 114 compares the profile with other profiles. The other profiles may be profiles of known typefaces, with each having a known typeface and a known point size. The step in box 116 uses the result of the comparisons in box 114 to obtain data identifying the dominant typeface of the text in the image. Fig. 10 shows steps in clustering an image profile with other profiles. The step in box 130 obtains a profile for an image that includes text having a dominant typeface. The profile may be obtained using a technique similar to that shown in Fig. 8 or using another technique that generates a profile indicating the dominant typeface. The step in box 134 clusters the profile with other profiles based on characteristics of the profiles. Fig. 11 shows steps in obtaining data indicating the dominant typeface of an image using information about skew. If skew of the image is not taken into consideration, the profile of the image may be shifted, making comparison with other profiles difficult. The step in box 150 obtains skew data indicating the skew of lines of text in an image. The step in box 152 uses the skew data in obtaining distribution data. This may be done by correcting the page orientation to deskew the image before measuring distances across connected components, or by shifting the angles at which distances are measured to compensate for the skew. The step in box 154 can obtain data indicating dominant typeface in a manner similar to Fig. 9. Figs. 12 and 13 show steps in using a profile indicating the dominant typeface of an image obtained by the steps in Fig. 8. Fig. 12 illustrates that the profile obtained in the step of box 160 can be stored on a memory medium by the step in box 162. This memory medium could be a rigid disk, floppy disk, ROM, CD-ROM, RAM, or any other suitable memory medium. In Fig. 13, the profile obtained in the step of box 170 is transmitted by the step in box 172 over a transmission medium, such as a network, a telephone line, a radio link, or other suitable medium. The general features described above could be implemented in numerous ways on various machines to obtain typeface data indicating dominant typeface. Fig. 14 shows system 220 in which the invention is implemented on a serial machine. Processor 222 is connected for receiving input data from input device 224 and for providing output data to output device 226. Processor 222 can be the CPU of a workstation, and input device 224 and output device 226 can be I/O devices. For example, input device 224 can be an image input device such as a scanner of a digital image source. Also input device 224 can provide a connection to a peripheral storage device or other storage or transmission medium from which model profiles that indicate distance as a function of direction can be received. Similarly, output device 226 can be a device for providing data obtained by processor 222, such as data indicating a dominant typeface or a profile that indicates distance as a function of direction in an image, to a storage or transmission medium. During operation, processor 222 executes instructions from program memory 228 and accesses data memory 230 to read and write data. Program memory 228 stores instructions for various operations of processor 222. Data memory 230 stores data structures as shown and can also temporarily store intermediate data used by processor 222 in performing its operations. Image data storing operation 232 receives image data through input device 224 and stores them in data memory 230, and image data modifying operation 234 modifies image data 260. Similarly, image data output operation 236 provides image data 260 to output device 226. The main typeface operation 238 identifies the dominant typeface of text in an image and obtains typeface data 266. Main typeface operation 238 may call profile forming operation 240, profile receiving operation 242, profile storing operation 244, and profile transmitting operation 246. Profile forming operation 240 obtains data defining a profile and stores the data in profile data 264. Profile receiving operation 242 receives data defining a profile from input device 224 and stores the received profile in model profiles 262. Profile storing operation 244 stores data defining a profile from profile data 264 onto a memory medium through output device 226. Profile transmitting operation 246 transmits data defining a profile from profile data 264 through output device 226. Distribution obtaining operation 248 may be called by profile forming operation 240 to obtain distribution data 268. Distance obtaining operation 250 may be called by distribution obtaining operation 248 or by skew obtaining operation 256 to measure distance in a direction in the image defined by image data 260. Profile comparing operation 252 may be called by main typeface operation 238 to compare a profile defined by data in profile data 264 with other profiles from profile data 264 or from model profiles 262. Profile clustering operation 254 may be called by main typeface operation 238 to cluster profiles in profile data 264 to find profiles having similar characteristics. Skew obtaining operation 256 may be called by main typeface operation 238 to obtain skew data indicating the skew of lines of text in an image; the skew data may be used in obtaining typeface data. Random location operation 258 may be called by distribution obtaining operation 248 to obtain starting locations from which distance obtaining operation 250 measures distance. Image data 260 in data memory 230 include data indicating a value for each pixel of an image. Image data 260 can be stored by image data storing operation 232. Model profiles 262 include data defining profiles of model images, such as images of text with known typeface and point size, and may be accessed by profile receiving operation 242 or profile comparing operation 252 as described above. Profile data 264 include data defining profiles stored by profile forming routine 240 as described above. Profile data 264 may also be accessed by profile storing operation 244, profile transmitting operation 246, profile comparing operation 252, or clustering operation 254, as described above. Typeface data 266 include data indicating or identifying dominant typeface as determined by main typeface operation 238 as described above. Distribution obtaining operation 248 stores distribution data 268 as described above. System 280 in Fig. 15 resembles system 220 in Fig. 14. System 280 includes host processor 282 connected to receive data from input device 284 and to provide data to output device 286. Host processor 282 is also connected to exchange data with parallel processor 290, which could, for example, be a Connection Machine from Thinking Machines Corporation. Parallel processor 290 includes processing units 294, each with local memory 292. Data defining an image can be stored in local memory 292 so that each pixel's value is stored in a respective processing unit's local memory. Each processing unit can store the respective value of one or more pixels in its local memory. Host processor 282 executes instructions from program memory 300 and accesses data memory 302 in performing image processing, as described above in relation to Fig. 14. Host processor 282 performs operations that include operating each pixel's respective processing unit in parallel to obtain distance data for use in obtaining typeface data indicating dominant typeface. Figs. 16 and 17 show operations that could be performed by system 22 of Fig. 14. Fig. 16 shows an operation that produces an average distance as a function of direction profile that indicates dominant typeface of text in an image. Fig. 17 shows an operation that uses the average profile to determine the dominant typeface of text in an image. The step in box 310 in Fig. 16 initializes the system and loads the image array which defines the image to be analyzed. If the image data are obtained in a different format than appropriate for the steps being performed, such as in a disk file format or a scanner output format, this step must convert it into an array or other appropriate format. The step in box 312 sets constants, including distance-limit; scan-values , indicating the number of directions to be used; and location-samples , indicating the number of locations to be used in characterizing the image. The constant distance-limit may be set to a value sufficiently greater than the interline spacing of the image. For example, in one set of images the technique has worked well with distance-limit set to 70. Because the directions are obtained by dividing 360° by scan-values , a larger value for scan-values detects image characteristics with greater resolution. Satisfactory resolution has been obtained for typeface identification with scan-values set to values from 60 to 120. Satisfactory results have been obtained with location-samples set to 3000. Greater values for location-samples require more computation but may not achieve greater accuracy, once an appropriate level of statistical significance has been reached. The resolution of the image received and the particular characteristic being measured may affect the value required to obtain satisfactory measurements. Appropriate values of scan-values and location-samples may be determined experimentally or by statistical analysis. In general, a satisfactory value for a given measurement on a given image can be obtained by increasing each of these constants until further increase does not improve reliability of results. The step in box 314 creates an array with scan-values size. This array will be used to store the scan directions of a segmented circle. To obtain the scan directions, the step in box 316 divides the value of the circle, 360°, by scan-values . Each direction can be stored in the array as an angle offset from some direction such as vertical down (i.e., 0° = ∝). The step in box 318 creates another array, the distance data array. This array also has a size of scan-values , and will be used to store the distance measurements for each direction. The step in box 320 resets the direction counter to zero, and increments the location counter to keep track of the number of locations sampled. The step in box 322 checks for when the location counter reaches location-samples. If the desired number of locations have not yet been sampled, the step in box 324 chooses another location. This may be a random or pseudo-random choice. The step of box 326 checks to make sure that the location is an appropriate choice for a starting pixel. For typeface identification, the starting pixel should be of the same value as the pixels within connected components, typically black. The technique of Figs. 16 and 17 identifies dominant typeface satisfactorily if each starting pixel is any black pixel or if each starting pixel is any black edge pixel. If the chosen pixel does not have the desired properties, no measurements are made from the pixel and the system returns to box 324 to obtain another location. If the pixel is an appropriate starting location, the step in box 328 begins an iterative loop that handles scan-values directions. For each iteration, the step in box 332 increments the direction counter to indicate a new direction. The step in box 334 sets limits for the height and width of the image array for the chosen pixel. These may indicate the outside edges of the image, so that measurements need not be taken beyond the edges of the image. The step in box 336 computes respective increments along the x and y axes for each increment along a line in the new direction. The step in box 338 starts the variables x-bar and y-bar at the first increment from the starting pixel along this line and resets the distance count dist . The step in box 340 begins an iterative loop by checking whether the length from the starting pixel is beyond any of the width, height or distance limits. If it is not, the current values of x-bar and y-bar are rounded to integers in order to access data defining a pixel in the image array. The step of box 344 checks to see if the pixel meets a criterion for stopping; for example, to measure distance across a black connected component, the stop criterion can be a white pixel. If the pixel does not meet the stop criterion, the step in box 346 increments x-bar , y-bar, and dist , and returns to box 340 to check for the limits for the next location. If the pixel meets the stop criterion, distance data dist indicating length from the starting pixel are stored in the distance data array by the step in box 348. The system then returns to box 328 until distance has been measured for scan-values directions. If distance has been measured in all directions, the distance data are combined with previous distance data in box 330 by obtaining data indicating the sum of the distance in each direction to the sums of previous distances in that direction, and the location counter is incremented in box 320. If location-samples locations have been sampled at the step in box 322, the step in box 350 obtains distribution data for each direction by dividing the total distance in the direction by the number of locations. These distribution data are used to form a profile of average distance as a function of direction for the image. The average profile is returned in the step in box 352. Fig. 17 shows an operation that uses the average profile to determine the typeface of text in an image. The step in box 380 obtains skew data indicating the skew of lines of text in the image, which can be taken into account in later computations or can be used to deskew the image. The step in box 382 computes a new average profile for the image, as described above in relation to Fig. 16. Box 384 begins an iterative loop that compares the profile from box 382 with each of a number of model profiles for known typefaces. The step in box 386 retrieves the next model profile for comparison. The step in box 388 computes the sum-of-squares difference between the model profile and the average profile from box 382. This can be done using the formula: to obtain a sum of squares, of which the square root could then be obtained as a measure of difference between the model profile and the current average profile. Other standard comparison techniques could be used, such as obtaining a difference in absolute values or any other conventional measure of distance. The sum-of-squares difference method corrects for small degradations in copy quality, such as a thickening or thinning of characters without bleeding or breaking, where the primary effect on the profile is a uniform shift up or down. The difference from box 388 is stored in the step in box 390 in a result array in a location accessible using the model profile's typeface. If there are no more model profiles to check in box 384, the step in box 392 find the smallest difference in the results array. An identifier of the typeface with the smallest difference is returned in the step in box 396. Figs. 18-21 show examples using the technique of Figs. 16. In each case, a profile of average distance as a function of direction is obtained using the technique of Fig. 16 on a sample of text. Fig. 18 shows an example of Bookman typeface. Fig. 19 is a plot of a profile for Bookman typeface, obtained using the technique of Fig. 16. Fig. 20 shows a sample of text in Avant Garde typeface and Fig. 21 shows a plot of a profile for Avant Garde typeface, also obtained using the technique of Fig. 16. Figs. 22-26 show examples of five different typefaces, in 12 point typeface at 12 dots per mm. Two of the typefaces are sans serif. Fig. 22 shows text in Avant Garde typeface. Fig. 23 shows text in Helvetica typeface. Three of the typefaces are serif. Fig. 24 shows text in Bookman typeface. Fig. 25 shows text in Bodoni typeface. Fig. 26 shows text in Palatino typeface. Model profiles were computed for each typeface using all the black pixels in a single scanned page of the typeface. These models were tried on thirty samples of text. Figs. 22-26 each show five samples of text in the respective typeface with various levels of degradation. Sample (C) in each figure shows the typeface in "normal" condition. Sample (B) shows a "light" version, made by photocopying the original two times using a "light setting". Sample (A) shows a "very light" version, made by photocopying the original four times using a "light" setting. Sample (D) shows a "dark" version, made by photocopying the original two times using a "dark" setting. Sample (E) shows a "very dark" version, made by made by photocopying the original four times using a "dark" setting. The technique of Figs. 16 and 17 was applied to six samples (except in one case, where it was applied to five samples) of each typeface: two "normal", one "light", one "very light", one "dark", and one "very dark." Several experimental conditions were tried. The technique was applied using 60, 90, and 120 directions for scan-values , and with 2500, 5000, and 10,000 black pixels sampled as location-samples . The tables below show how many times samples of each typeface were classified as each known typeface. For example, in Table 1 Avant Garde typeface was correctly classified all six times when tested using 60 directions at 2500, 5000, and 10,000 samples. Samples in Helvetica typeface when tested using 60 directions were correctly identified twice when the test included 2500 and 5000 samples, and three times when the test included 10,000 samples. Table 1 shows the result of running the method using 60 scan-values. The three sets of numbers show results when location-samples were taken at 2500, 5000, and 10,000 pixels, respectively. Avant Garde Helvetica Bookman Bodoni Palatino Avant Garde 6,6,6 Helvetica 3,3,2 2,2,3 0,1,1 1,0,0 Bookman 4,4,3 2,2,3 Bodoni 5,5,5 1,1,1 Palatino 6,6,6 Table 2 shows the result of running the method using 90 scan-values . The three sets of numbers show results when location-samples were taken at 2500, 5000, and 10,000 pixels, respectively. The technique was applied to only five samples at 5000 pixels, with only one "normal" sample. Table 3 shows the result of running the method using 120 scan-values . The three sets of numbers show results when location-samples were taken at 2500, 5000, and 10,000 pixels, respectively. It should be noted that almost all the misclassifications were for degraded quality images--"very light," "light," "dark," or "very dark." None of the "normal" samples was incorrectly classified when sampling 10,000 pixels. A number of the classification errors occur on data of very poor quality, such that even a human observer would have a hard time recognizing the typeface. The invention could be applied in many ways. Fig. 27 illustrates an application of the invention in a copying machine. Avant Garde Helvetica Bookman Bodoni Palatino Avant Garde 6,5,6 Helvetica 3,2,2 3,2,3 0,1,1 Bookman 2,3,4 2,0,1 2,2,1 Bodoni 5,4,5 1,1,1 Palatino 0,1,1 6,4,5 Avant Garde Helvetica Bookman Bodoni Palatino Avant Garde 6,6,6 Helvetica 2,1,2 3,4,3 1,0,0 0,1,1 Bookman 4,2,4 0,1,0 2,3,2 Bodoni 5,5,5 1,1,1 Palatino 1,0,1 5,6,5 In Fig. 27, copying machine 400 includes scanner 402, image processing system 404, and printer 406. Scanner 402 can produce data defining an image of an input document. Image processing system 404 can be implemented as shown in Fig. 14 or in Fig. 15, and can, in addition to identifying dominant typeface, use optical character recognition techniques to identify characters in the document. Image processing system 404 might also apply techniques to detect character size and positioning and to produce data defining a corrected image in which a character in the input document image is replaced by a correct version of the same character from the identified dominant typeface at the appropriate size, position, and skew. The data defining the corrected image could then be provided to printer 406 to print an output document. The invention has been described in relation to implementations in which operations are performed on data defining an image to obtain data indicating information about dominant typeface of text in the image. The invention might also be implemented with specialized circuitry connected to photosensors for directly obtaining data indicating information about dominant typeface of text in an image. The invention has been described in relation to software implementations, but the invention might be implemented with specialized hardware. The invention has also been described in relation to implementations in which distances are measured from randomly selected locations. The starting locations for distance measurement could alternatively be selected with some other criterion, such as all pixels interior to connected components, all pixels at edges, or every Nth interior or edge pixel. Furthermore, it might not be necessary to measure distance in each direction from a given location; instead, distance might be measured from a different set of starting locations for each direction. The invention has been described in relation to implementations in which distance is measured across a 360° range of equally separated directions. It might be possible to use a 180° range of directions. It might also be possible to identify a set of directions that are not equally separated but that provide the necessary information to indicate dominant typeface. The invention has been described in relation to implementations in which distribution data for each direction is obtained by averaging measured distances. Other techniques might be used to obtain distribution data. For example, other measures of central value could be obtained, such as a mode or a median. A measure of variance could be obtained, such as a standard deviation. A mixed measure of central value and variance could be obtained, such as distances at which the frequency is a number of decibels below the mode. In general, distribution data can be obtained after all distance measurements are obtained or, to reduce data storage requirements, distance measurements can be combined as they are obtained, such as by summing. The invention has been described in relation to implementations in which data defining a profile of distribution data as a function of direction indicates dominant typeface. Other techniques might be used to obtain data indicating dominant typeface. The directions in which maxima and minima of distribution data occur, for example, might indicate dominant typeface.

Claims: An image processing machine comprising: memory for storing image data defining an image that includes text with characters having a dominant typeface, the image including connected components (40) that form the text; and a processor (222,280,282) connected for accessing the memory, the processor comprising: distribution obtaining means for using the image data to obtain, for each of a plurality of directions, respective distribution data indicating a distribution of distances across connected components (40) in that direction, the distribution obtaining means obtaining each direction's distribution data by measuring distances across a set of two or more connected components in that direction; the distribution obtaining means measuring each distance across one of the connected components (40); the set including connected components (40) in two or more of the characters in the text; and typeface obtaining means for using the distribution data for the plurality of directions to obtain typeface data indicating the dominant typeface. A method of performing image processing on an image that includes text with characters having a dominant typeface, the image including connected components (40) that form the text; the method comprising: measuring distances across connected components (40) in each of a plurality of directions and using the measured distances to obtain, for each direction, distribution data indicating a distribution of distances across connected components (40) in that direction for the image; the step of measuring distances comprising measuring distances across a plurality of connected components (40), each distance being measured across one of the components; the plurality of connected components (40) being in a plurality of the characters in the text; and using the distribution data for the plurality of directions to obtain typeface data indicating the dominant typeface.

Inventors: HUTTENLOCHER DANIEL P, WAYNER PETER C, HUTTENLOCHER, DANIEL P, WAYNER, PETER C.
Applicants: XEROX CORP, XEROX CORPORATION
Application year: 1992
Main classification: G06K9
IPC codes: G06K9