Abstract:
This invention discloses image analysis techniques supporting the interpretation of marks on a large drawing board for purposes of controlling a Board transcription device. In order for users to control such a transcription device by writing on the Board itself, the system recognizes certain markings and directives drawn by users in accordance with an agreed-upon format for control diagrams conveying information as to the parameters of transcription. In the embodiment herein described, nested squares, spatial region indicators, and symbolic annotation analysis area used to convey command parameters. The present invention demonstrates line drawing analysis techniques that support grouping and manipulation of symbolic tokens in a Scale-Space blackboard data structure.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of Application Ser. No. 08/303,929, filed Sep. 9, 1994, now abandoned. 
     This application is a related to U.S. patent application Ser. No. and U.S. Patent Application Serial No. both coassigned and concurrently filed, and incorporated by reference herein. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to controlling a device, particularly a computing device, through hand drawn markings on a whiteboard or blackboard device. More specifically, the invention relates to image analysis techniques for interpreting marks for purposes of controlling devices. 
     BACKGROUND OF THE INVENTION 
     In collaborative working environments, several users frequently wish to view and manipulate displayed information simultaneously. Whiteboards and Blackboards (hereafter referred to as “Boards”) are widely used to maintain hand drawn textual and graphic images on a “wall-size” surface. The Board medium offers certain properties that facilitate a variety of interactive work practices: markings are large enough to be viewed by several people; markings can be edited by erasing and redrawing; the surface is immobile, so does not get lost, crumpled, torn, or blown by wind; the surface is readily erased, is completely reusable, and (practically) does not wear out. However, one drawback to using a Board is that information is not easily transferred to other media. Thus, it is not currently possible to hold a conversation with someone while maintaining a record of the conversation in text and graphics on a Board and then quickly, easily, and automatically transfer the record to paper or other portable and storable medium. 
     Existing methods for accomplishing this task are cumbersome, time-consuming, and inconvenient. One can simply transcribe by hand, onto paper, any or all of the text and graphics residing on the Board. This can be time-consuming, and suffers from errors due to mistakes in human reading and writing. Or, one can photograph the Board with a camera. This requires having a camera at hand, introduces the delay of developing the film, can be expensive if an “instant” camera is used, and is subject to poor quality rendition due to improper focus and exposure. A camera further usually produces an image of greatly reduced size that can be difficult to read. 
     Alternatively, “wall-size” sheets of paper, such as poster pads, lead to a relatively permanent and portable record of what was written, but these sheets of paper are large and cumbersome, and do not permit erasure during image creation. 
     A copy-board device provides a writing surface which can be transcribed into paper hardcopy, but these are currently conceived as conspicuous portable whiteboards that displace rather than leverage existing, built-in Boards. 
     The solutions discussed above further do not aid in transferring the image from the Board into an electronically usable form. 
     Concurrently filed U.S. patent application Ser. No. (Attorney Docket No. D/94266) offers motivations and specific technical details for a device to transcribe marks on a Board into electronic form. In summary, a video camera is mounted on a pan/tilt head. High resolution tiles are obtained by zooming in the camera on patches of the image. These are later pieced together to form a full size high resolution composite image. Perspective distortion, effects of uneven lighting, and tile overlap are handled by image processing operations. 
     Such a transcription device is useful because an electronic version of a Board image provides variety and flexibility in the further use of the image data. For example, an electronic image can be hardcopied, transmitted by fax, stored to a file, transferred to an electronic workstation, or projected onto a screen. Moreover, prior to any of these operations the image itself may be processed, for example select out just a region of the image, to select just certain colors, to enhance or rectify the line work, to reorder items in a list, and so forth. 
     The wealth of operations made available by the fundamental ability to transcribe a Board image raises the issue of control: How is the user to specify operations to be done, and when? 
     Since the Board transcription and image processing operations are computer-based, one possibility is for users to retire to their computers in order to control these functions. This solution is undesirable for several reasons. First, it forces users to break the cadence of their work at the Board in order to address a computer console. Second, either a console must be provided at the Board location, or else users must travel some indeterminate distance to where one is available. Third, many Board users are likely to be unfamiliar and/or uncomfortable either with computers in general, or else with the particular keyboard and mouse commands necessary to operate the program. 
     A second type of user interface consists of a dedicated control panel mounted adjacent to the Board. If the control panel consists of labeled buttons, these can be associated with a modest set of possible operations such as directing the transcribed bitmap to one of a handful of printers or file directories. Greater flexibility would be obtained by including a keyboard with the control panel, but this begins to present a daunting edifice to novice users. Nonetheless, for some incarnations of a Board transcription device a dedicated control panel is probably appropriate. 
     In the system of the present invention, however, a third alternative exists which is in several ways ideally suited for seamless creation, capture, and electronically mediated use of images originating on a whiteboard. The user interface is to consist of marks drawn by the user on the Board itself. For example, in the simplest case the user might draw a special “button” symbol, and then draw a check mark inside when the button is to be “pressed.” The system, knowledgeable about buttons, would act upon the data based on the button press. Enhanced functionality may be achieved by annotating the button symbol with further directives, or by specifying different kinds of button for different operations. 
     The previously described interface does not eliminate the need for users to possess knowledge of how to communicate with the system in this diagrammatic fashion, but this knowledge may be more accessible and more easily assimilated by many Board users than any control panel or computer console based interface. 
     A diagrammatic interface exploits the user&#39;s existing skills in drawing on the Board, and furthermore can offer extremely simple access to basic functionality and introduce incrementally the greater diagrammatic complexity required to fine tune greater functionality. Finally, a diagrammatic interface consisting of marks drawn on the Board itself is best suited to providing spatial control directives, such as to extract a region of the image. 
     A diagrammatic user interface to a Board transcription system hinges on the ability of a computer program successfully to interpret marks on the Board. An additional advantage of the present invention includes tolerance to variability and spurious marks made by a human user. 
     Relatively crude yet effective basic functionality can be had by the application of very simple image processing techniques. The “BrightBoard” system, described in “Controlling Computers by Video” by Quentin Stafford-Fraser of EuroPARC, employs a video camera pointed at a fixed position on the board. User-experts execute a graphical program at a computer console to denote special regions of the board to serve as “sensitive locations,” which are typically the interiors of buttons drawn on the board. Functionality is associated with sensitive regions at setup time. Then, in operation, a simple routine runs continuously to measure the net pixel lightness of the sensitive region, which is assumed to cross a threshold when a dark enough mark is made within it. 
     The image analysis techniques used in the BrightBoard system provides a basic level of control via marks on the Board, depending on the number of sensitive regions one wishes to define. However there are several drawbacks. First, the sensitive regions must be set up in advance and this Board space reserved for the system until the setup configuration is modified. Second, the functionality associated with simple button toggle is limited. Third, the detection of button presses by a change in lightness in sensitive regions is susceptible to false positives caused by shadows or changes in room lighting, and to false negatives due to thinly drawn and lightly colored marks. 
     There is therefore need for greater sophistication in the image analysis supporting a diagrammatic user interface to a whiteboard transcription system. The power of the interface can be greatly enhanced by the application of computer vision techniques in the geometric analysis of the marks on the Board. Users should be able to draw buttons on the Board anywhere and at any time, they should be able to press buttons by making “X” or check marks in them, and they should be able to make more complex diagrams to control symbolic and geometric functionality such as specifying which printer to hardcopy to, or the region of a Board to be extracted. 
     Diagrammatic user interface interpretation is a difficult computer vision problem because of the wide range of variation found in meaningful hand drawn commands. Symbols and text can occur in any location and at any spatial scale (size), ostensibly straight lines seldom are truly straight, supposedly continuous lines have spurious gaps and branches, and the conventions of formal geometry (such as that a square consists of two sets of parallel line segments meeting at four 90 degree corners) are seldom obeyed. Existing computer vision and document image analysis techniques perform inadequately on the Board diagrammatic user interface analysis task under normal operational conditions in which users are not likely to be especially careful about the precision and accuracy of their diagrammatic command drawings. In addition, because of the real time nature of this task, the image analysis techniques must be inherently efficient in complexity (such as avoiding combinatoric complexity in the number of marks on the board). 
     The present invention describes a novel application of computer vision techniques supporting interpretation of hand drawn commands under an open-ended class of diagrammatic user interface designs. The components of these interface designs may include special hand drawn symbols, and curvilinear connectives. Accordingly, the techniques of this invention support recognition of hand drawn command symbols, and tracing of curvilinear connectives. The techniques offered herein further lead to a greater degree of robustness in the machine interpretation of hand drawn diagrams than has previously been demonstrated in the art. 
     SUMMARY OF THE INVENTION 
     The present invention provides a method for controlling devices by interpreting hand drawn marks on a scanned surface. The method includes determining a hand drawn symbolic token representing an action, determining when the symbolic token is selected, and performing the action when it is selected. The method further provides for indicating a spatial area of the scanned surface associated the symbolic token, and for performing the action with respect to that spatial area. 
     One object of this invention is to provide human users with the ability to control computing devices and transcription devices through hand drawn images on a Board. These images may be drawn at any location on the Board, at any time, and provide a method for selecting control operations from the Board. Symbolic tokens are based upon geometric symbols that may be easily hand drawn and detected by spatial and symbolic analysis procedures. 
     A further object of the invention is to provide recognition of control diagrams in hand drawn images, and to provide a method for controlling symbolic and geometric functionality to the Board user. 
     The following description, the drawings and the claims further set forth these and other objects, features and advantages of the invention. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 shows a general surface scanning system. 
     FIG. 2 is a flowchart which shows a general method for producing a binary rendition of a Board from a set of scanned image sections. 
     FIG. 3 shows a board image including a hand drawn button consisting of two nested squares. 
     FIG. 4 shows a complex hand drawn command structure including lines leading to symbolic textual annotations. 
     FIG. 5 shows an image with a selected region including hand written text lines. 
     FIG. 6 shows an image with demarcation lines which may be used to designate a region of the image. 
     FIG. 7 describes the basic steps involved in using the features illustrated in FIGS. 3-6. 
     FIG. 8 presents the overall functional architecture  180  of the present invention. 
     FIGS. 9-14 are flowcharts which show steps in the top level task control module for directing the general operation described in flowchart of FIG.  7 . 
     FIG. 15 is a flowchart which shows steps used to identify curve fragments. 
     FIG. 16 shows some typical hand drawn nested box buttons (NBB). 
     FIG. 17 illustrates general features required of a NBB. 
     FIG. 18 illustrates alignment of curvilinear tokens. 
     FIG. 19 is a flowchart which shows steps in determining a NBB, and whether or not it is selected, with the steps illustrated by FIGS. 20-27. 
     FIG. 28 is a flowchart which shows steps for simple curve tracing. Simple curve tracing is illustrated by FIG.  29 . 
     FIG. 30 is a flowchart which shows steps for beam search contour tracing. 
     FIGS. 31-33 illustrate beam search contour tracing. 
     FIG. 34 is flowchart which shows steps for tracing of branching contours. 
     FIG. 35 shows examples of other possible command symbols 
    
    
     DETAILED DESCRIPTION 
     A. Conceptual Framework 
     The following conceptual framework is helpful in understanding the broad scope of the invention, and the terms defined below have the meanings indicated throughout this application, including the claims. This conceptual framework is a modification and extension of that set forth in the following copending, coassigned U.S. patent application, incorporated herein by reference: U.S. patent application Serial No. (Attorney Docket No. D/94266). 
     B. General System Description 
     FIG. 1 shows a general Board scanning system  50  in which the techniques of the present invention may be employed. It will be clear that the techniques herein described may further be applied to other types of systems, as well. For example, the techniques described may be used to interpret control commands via marks on paper in a paper-based user interface. The techniques may also be used as command entry interface for pen-based computing systems. The discussion herein, however, will be primarily described in terms of the Board scanning system described below. 
     More detail describing the features and operation of the system in FIGS. 1 and 2 may be found in copending, coassigned U.S. patent application Ser. No. (Attorney Docket No. D/94266), previously incorporated. 
     A board  52  accepts markings from a user  51 . Camera subsystem  54  captures an image or images of the Board, which are fed to computer  56  via a network  58 . In general, the resolution of an electronic camera such as a video camera will be insufficient to capture an entire Board image with enough detail to discern the markings on the Board clearly. Therefore, several zoomed-in images of smaller subregions of the Board, called “image tiles,” must be captured independently, and then pieced together. 
     Camera subsystem  54  is mounted on a computer-controlled pan/tilt head  55 , and directed sequentially at various subregions, under program control, when an image capture command is executed. Camera subsystem  54  may alternately comprise an array of fixed cameras, each directed at a different subregion or subregions. For the discussion herein, camera subsystem  54  may be referred to as simply camera  54 . 
     The “raw” image tiles will in general overlap one another, contain perspective distortion due to the off-axis viewpoint of the camera, and contain uneven lightness levels across the board between foreground (written marks) and background (unmarked board) due to uncontrolled lighting conditions and reflections. The flowchart of FIG. 2 shows a general method for producing a binary rendition of the Board from a set of scanned image sections, including compensation for the above described effects. In step  100 , the scanned image sections are captured as “tiles.” Each tile is a portion of the Board image scanned by a camera, such as camera  54 . A Board is captured as a series of tiles. The tiles slightly overlap with neighboring tiles, so that the entire image is scanned with no “missing” spaces. The location of each tile is known from the position and direction of the camera on the pan/tilt head when the tile is scanned. The tiles may be described as “raw image” or “camera image” tiles, in that no processing has been done on them to either interpret or precisely locate them in the digital image. In step  102  center-surround processing is performed on each camera image tile. Center-surround processing compensates for the lightness variations among and within tiles. 
     Step  104  finds corresponding “landmarks” in overlapping tiles. “Landmarks” are described as marks on the Board which may be used to provide the correct orientation and overlap position of neighboring tiles. Landmarks that appear in a region of overlap between two tiles will be used to “match up” those two tiles to reconstruct the image. 
     Step  106  solves for perspective distortion corrections that optimize global landmark mismatch functions. This step corrects for errors that occur in the dead reckoning of the tile location in the image. The transformation is weighted by a confidence in the location of each landmark, which is obtained in the previous step. Step  108  performs perspective corrections on all the tiles using the perspective transformation determined in step  106 . 
     In step  110 , the corrected data for all the tiles is written into a grey-level Board rendition image. In step  112 , this grey-level image is thresholded, producing a binary, electronically manipulable rendition of the Board image. 
     In the discussion hereinafter, the actual steps of converting a Board image to a digital image will be assumed to be implemented as previously described. 
     C. General Features and Architectural Overview 
     FIGS. 3-6 illustrate general features of the present invention. Although the techniques of this invention support an open-ended class of design conventions for users to specify parameters and execute commands diagrammatically, for the purposes of illustration the techniques of the invention will be demonstrated with the particular command conventions shown in FIGS. 3-6. 
     Further, it will be clear to one of skill in the art that while the discussion below describes acting upon a selection in terms of transcribing portions of the image, the system may alternatively perform other types of actions upon receipt of a pressed button command, depending on the desires of the developer. Further, different shapes of buttons may conceivably be used for different command. This, however, would add to the complexity of the analysis and may adversely affect system performance. 
     FIG. 3 shows a board image  150 . The heart of a user&#39;s command in the present embodiment is a command designator comprising a hand drawn button consisting of two “nested” squares-one square immediately inside the other. This is referred to as a NESTED-BOX-BUTTON, or NBB  152 . 
     Drawing an “x” or a checkmark in the button, as shown by button  154 , initiates image transcription. 
     Clearly other geometric shapes may be used as command designators in addition to or replacing the boxes. For example, any of the geometric shapes shown in FIG. 35 provides a non-exhaustive set of examples of shapes that may form suitable buttons, depending upon the desires of the system designer. Some shapes, particularly nested similar shapes, are easier to reliably detect. Nested boxes, however, have an added advantage of being easy to construct reliably, and provide an easy to create interface for even an untrained user. 
     Optionally, a number of auxiliary parameters or directives may be attached to the button via curvilinear lines. These may lead to image demarcation symbols, to directive alphanumeric characters, or to text. For example, FIG. 4 shows a complex NBB  158  including lines leading to symbolic textual annotations directing that hardcopy be sent to a printer “daily planet,” that an image file be stored called “test.im,” and that only red and blue colors be transcribed. Analysis of the region associated with these curvilinear lines may be performed through a variety of methods. For example, applicant&#39;s copending, coassigned U.S. Patent Application Serial No. (Attorney Docket No. D/94267Q), incorporated herein by reference, provides a method for determining the segmentation of text lines in the image. Such information may be used to extract text from the image for recognizing by a text recognition system. 
     FIG. 5 shows an image  160 , a portion of “A list” in “Frog and Toad Together,” by Arnold Lobel, ©1972, HarperCollins Publishers. Image  160  has a region  162  encompassed by curvilinear lines, selected by NBB  164 . In FIG. 6, a portion of image  166 , shown by demarcation lines  168 , is attached to NBB  170 . In FIG. 6, for example, these demarcation lines may be used to designate that only the region encompassed by, or inside, demarcation lines  168  is to be transcribed. 
     FIG. 7 describes the basic steps involved in using the features illustrated in FIGS. 3-6. Step  174  finds a NBB, such as NBB  152 . Step  175  checks to see if the NBB is selected. If it is, step  176  determines spatial areas associated with the selected NBB, such as area  162  in FIG.  5 . Step  177  determines text segments that are associated with the selected NBB, such as text string  159  in FIG.  4 . Finally step  178  checks for other or new NBBs to perform the analysis for. 
     FIG. 8 presents the overall functional architecture  180  of the present invention. The core image analysis technique for interpreting diagrammatic commands under the present invention is spatially focused manipulation of “symbolic tokens,” compact symbolic descriptors of portions of the image, in this case marks and combinations of marks. Information about the content of image  182  is maintained as symbolic tokens residing in the Scale-Space blackboard (SSB) data structure  184 . These symbolic tokens serve as a resource for spatial analysis procedures  186 , which may at times contribute new tokens to the Board data structure. At the top level, task control module  190  directs spatial analysis procedures  186  to collect information about image  182  in order to build descriptions of the aspects of the image that are relevant to the immediate task. 
     The Scale-Space blackboard data structure  184  facilitates spatial and geometric computations by providing indexing of symbolic tokens on the basis of spatial location and scale. These tokens are compact symbolic descriptors of abstract image events such as the occurrence of curves, corners, nested boxes, characters, etc. The SSB data structure  184  is further described in Saund, E., “Symbolic Construction of a 2-D Scale Space Image,” IEEE TPAMI, V. 12 No. 8, August 1990. For convenience, in practice several SSB data structures may be maintained, each holding different kinds of information reflecting different stages of processing. 
     A large and extensible visual feature library  188  is maintained in connection with spatial analysis procedures  186 , with a knowledge base of prototypical spatial events which can be labeled explicitly by placing new tokens on the SSB. Table 1 lists types of major analysis procedures employed in the present invention. These spatial analysis procedures underlie the major image analysis tasks of recognizing command symbols, tracing curvilinear paths, and segmenting lines of handwritten text. 
     For example, a procedure for finding corners may identify curvilinear tokens in a specified region of the SSB, and label pairs of tokens that form a corner-shaped configuration. For each such pair, a new CORNER token may be created and placed in the appropriate location on the SSB in SSB data structure  184 . 
     
       
         
               
             
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Major Visual Analysis Procedures 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 identify tokens near specified location 
               
               
                   
                 identify tokens in specified direction from given spatial location 
               
               
                   
                 identify tokens aligning with specified token 
               
               
                   
                 select tokens with specified orientation 
               
               
                   
                 select tokens with specified curvature 
               
               
                   
                 select tokens with specified scale 
               
               
                   
                 label pairs of line tokens forming a corner configuration 
               
               
                   
                 label pairs of line tokens forming a parallel configuration 
               
               
                   
                 label pairs of line tokens forming a T configuration 
               
               
                   
                 label pairs of line tokens forming an X configuration 
               
               
                   
                 label pairs of corner tokens forming a nested corner configuration 
               
               
                   
                 label chains of aligning tokens forming a curvilinear arc 
               
               
                   
                 label groups of tokens forming a denseley textured region 
               
               
                   
                 determine whether appropriate nested corner and parallel tokens 
               
               
                   
                 are present to form two nested boxes 
               
               
                   
                 trace chains of tokens meeting end-to-end 
               
               
                   
                   
               
             
          
         
       
     
     The top level task control module  190  directs the application of spatial analysis procedures  186  according to a task execution strategy defined for the specific diagrammatic user interface specification. Associated with task control module  190  is a temporary store, spatial focus cache  192 , which maintains state as to what button boxes or other task level objects are believed to be in the image, as well as information about locations of the image requiring examination in detail as processing resources become available. 
     D. Task Control 
     The top level task control module shown in FIGS. 9-14 directs the application of spatial analysis procedures to implement the general operation described in flowchart of FIG. 7 according to a task execution strategy defined for the diagrammatic user interface specification shown in FIGS. 3-6. More detail for specific analysis procedures is described in the following sections. 
     In FIG. 9, step  420  processes the NBB visual appearance for every NBB on the Board, as will be described in relation to FIG.  10 . Step  422  looks for new NBBs on the Board. This step may be performed periodically by checking through all tokens on the board, or may be performed for sections of the board where activity is known to have occurred. 
     Step  424  of FIG. 10 verifies that the NBB exists on the Board, and if the NBB is no longer present at step  426 , removes the NBB from the cache in step  428  and returns. If the NBB is still there, step  430  checks to see the button press status. If the button is newly pressed in step  432 , then the command parameter analysis, as described in relation to FIG. 11, is performed. Step  436  outputs a transduce image command plus the command parameters before returning. 
     FIG. 11 analyzes command parameters associated with the NBB. Step  438  performs a spatial subregion indicator analysis, as will be described in relation to FIG.  12 . Step  440  performs symbolic annotation analysis, as will be described in relation to FIG.  13 . 
     In FIG. 12 the spatial subregion indicators are analyzed to determine a spatial subregion associated with the NBB. Step  442  looks for spatial subregion indicator curvilinear strokes. If spatial subregion indicator strokes are found in step  444 , then the strokes are traced in step  446 . The spatial subregion polygon indicated by the tracing is added to the command parameter specification associated with that particular NBB in step  448 . 
     A determination and analysis of symbolic annotation regions associated with the NBB is performed by the steps of FIG.  13 . Step  450  looks for curvilinear strokes descending from the bottom center of the NBB being analyzed. If a stroke is found in step  452 , step  454  traces the curvilinear line to the next junction. Step  456  traces to the end of rightward extending branches, and step  458  segments out symbolic annotation text regions, for example using the method described in previously incorporated U.S. Pat. No. (Attorney Docket No. D/94267Q). Step  460  sends the extracted bitmap to a text recognition module, and step  456  adds symbolic annotation control parameters to the command parameter specification associated with this particular NBB. 
     If the junction was a “T” junction, at step  464  the system returns to trace the remaining leg of the curvilinear stroke. 
     Finally, FIG. 14 looks for new NBBs on the Board. Step  466  groups aligning tokens that have been newly added to the Board. Step  468  detects nested upper-left corners among the newly added tokens. In step  470 , every nested upper-left corner in the cache is checked for a NBB. If a NBB is found in step  472 , it is installed in the cache in step  474 , and its visual appearance will be analyzed in step  420 . 
     E. Processing Techniques 
     The following description and figures describes image processing techniques that may be used to identify tokens, trace curves and corners, and segment handwritten text lines to implement the techniques of task control as described above. 
     i. Basic Curvilinear Processing 
     The most primitive type of token is called a PRIMITIVE-CURVE-FRAGMENT, representing relatively straight sections of curves having no internal junctions or crossings with other curves. The steps shown in FIG. 15 are used to identify curve fragments. 
     Step  200  first thins the lines of the drawing using morphological erosion, a technique well-known to practitioners of computer vision. Step  202  detects curve junctions using a morphological hit-miss transform with masks that reflect junction configurations of  3 × 3  pixels. 
     Step  204  traces curves between junctions, using any one of many curve tracing methods well known in computer vision literature. Step  206  detects corners on curves. Techniques for doing this are also available in computer vision literature. 
     Step  208  breaks curves at corners, leaving elementary fragments to be labeled as PRIMITIVE-CURVE-FRAGMENT type tokens, which are written onto the SSB data structure. 
     These processing steps are further described in copending, coassigned patent application Ser. No. 101,646 “Method for Dynamically Maintaining Multiple Structural Interpretations in Graphics Systems,” to Saund, et al. herein incorporated by reference. 
     ii. Command Symbols 
     In the present invention, command symbols take the form of nested boxes and associated curvilinear lines, as described in relation to FIGS. 3-6. Tokens are identified as previously described, each having a location, orientation, length, and curvature. In the space of all possible positions, each pair of tokens may be measured to evaluate how good a corner they form. (This evaluation of “how good a corner” may be based on threshold parameters set by the system designer.) 
     In the present embodiment, as explained above, nested boxes are used to denote command buttons. The techniques described below demonstrate only the detection of NBB command symbols, but the technique clearly extends readily to a broad class of possible command symbol shapes, such as those shown in FIG.  35 . 
     A NBB may be described as two boxes which are nested within one another. Symbolic tokens are generally made up of other, smaller, subtokens. FIG. 17 shows an example of subtokens which make up a nested box button symbolic token. A NBB  210  is established if NESTED-CORNER subtokens, such as  212 , and PARALLEL-LINE subtokens, such as  214 , are found in appropriate proximity and orientation with respect to a hypothesized button center  216 . Users, however, are typically not especially careful about the precision and accuracy of the diagrammatic command drawings. FIG. 16 shows some typical hand drawn nested box buttons that may be input by a user, illustrating some of the difficulty in interpreting a diagrammatic user interface in typical computer vision systems. For example, some corners such as  262  and  264  may not completely meet, just as corners  274  and  286  have some lines that extend beyond the corner. Some lines, such as  280 , may be more curved than straight, or may, like  282 , contain breaks. The techniques of the present invention allow any of the NBBs shown in FIG. 16 to be correctly interpreted. 
     FIG. 19 describes the steps in determining a NBB, and whether or not it is selected, with the steps illustrated by FIGS. 20-27. Step  222  begins by finding a candidate location. In the present embodiment, an “upper left” nested corner  212  is searched for. Filters may be used on the various corners pairs to remove all tokens that can&#39;t be part of an upper left corner of an NBB. Upper left corners which are nested—one nearly inside the other—are selected. The center of a possible NESTED-BOX-BUTTON hypothesized by the location and size of the nested upper left corner is chosen as a candidate location. In FIG. 20, location  292  is the hypothesized button location for presumed NBB  284 , and selected as a candidate location. 
     Step  224  gathers all PRIMITIVE-CURVE-FRAGMENT tokens lying within a prespecified scale-normalized distance from the candidate location, as shown in FIG.  21 . 
     Step  226  forms LINEAR-STROKE type tokens by grouping PRIMITIVE-CURVE-FRAGMENT tokens that clearly align with one another, shown in FIG.  22 . To determine “clearly aligned” tokens, tokens are examined pairwise. As shown in FIG. 18, for some pairs of tokens taken alone, such as tokens  257  and  258 , which meet end to end with the same orientation, there is little question over their alignment. If one token approaches another at a slight angle, however, such as token  259  and token  258 , it introduces some ambiguity, because token  258  could be aligned with either token  257  or  259 . The system must therefore look at pairs of tokens, and then look in the vicinity of aligned pairs to find any other candidates for alignment. If the “second best” token is much less aligned than the first, and if the first two tokens are mutually the “best” alignment, then the first two are clearly aligned. If the second best is only slightly less well aligned, however, then all three tokens may be determined to not be “clearly aligned.” 
     Returning to FIG. 19, step  228  forms CIRCULAR-ARC type tokens by grouping PRIMITIVE-CURVE-FRAGMENT tokens that align with one another along a circular arc, as shown in FIG.  23 . Unlike LINEAR-STROKE tokens, CIRCULAR-ARC tokens may overlap and share support at the level of PRIMITIVE-CURVE-FRAGMENT tokens. 
     Step  230  forms CORNER type tokens, shown in FIG. 24, by finding pairs of LINEAR-STROKE and/or CIRCULAR-ARC tokens that form a corner configuration. In order to support a CORNER, a CIRCULAR-ARC token has to form only a shallow arc. In step  232 , NESTED-CORNER type tokens are formed by finding nested pairs of CORNER tokens. In FIG. 25, dotted lines  294  and  295  show tokens grouped into NESTED-CORNERs. 
     Step  234  forms PARALLEL-LINE type tokens by finding pairs of LINEAR-STROKE and/or CIRCULAR-ARC tokens lying parallel. Dotted lines  296  and  297  in FIG. 26 show PARALLEL-LINE type tokens. 
     In step  236 , the system checks whether NESTED-CORNER and PARALLEL-LINE tokens are found in approximate locations and orientations consistent with the presence of two nested squares. If so, a new nested-box-button, or NBB token, is formed in step  240 . 
     If a NBB has been found, step  242  checks for the presence of LINEAR-STROKE and/or CIRCULAR-ARC tokens forming an “X” or checkmark configuration, in the approximate appropriate location with respect to the location and size of the NBB, as shown in FIG.  27 . 
     Alternatively, other symbols, such as simple slash marks, could also be used to select buttons. The flexibility of the parameters allows the system designer discretion in choosing specialized diagrams for what ever system the user interface is to be used on. 
     This token grouping approach differs from model-based matching approaches conventionally employed in the computer vision community, and succeeds in recognizing imprecisely drawn command symbols in the presence of confounding data such as check marks and the curvilinear lines connecting to the nested box button shapes. 
     iii. Tracing Curvilinear Paths for Spatial Subregion analysis 
     Curvilinear paths are useful in diagrammatic user interfaces for encircling regions and for forming links among command symbols and other directives such as text annotations. The basic token grouping approach underlying this invention, described previously, supports tracing of simple curvilinear paths, curvilinear paths crossed by other lines, branching curvilinear paths, and broken curvilinear paths. 
     Curve tracing makes use of PRIMITIVE-CURVE-FRAGMENT tokens and CURVE-END tokens associated with each end of each PRIMITIVE-CURVE-FRAGMENT. 
     Input to a curve tracing routine consists of a PRIMITIVE-CURVE-FRAGMENT  312  in FIG. 29, and one of its associated CURVE-ENDs  314 , which is called the “CURRENT-STARTING-END.” Simple curve tracing operates as shown by the flowchart in FIG. 28, and illustrated by FIG.  29 . 
     In step  300  the other end  316  from the starting end  314  of the PRIMITIVE-CURVE-FRAGMENT  312  is identified. This is called the CURRENT-FAR-END. 
     Step  302  locates CURVE-ENDs belonging to other PRIMITIVE-CURVE-FRAGMENTs near the CURRENT-FAR-END. In FIG. 29, CURVE-ENDs  317  and  318  are detected. 
     Step  304  checks the alignment of the other PRIMITIVE-CURVE-FRAGMENTs with the current PRIMITIVE-CURVE-FRAGMENT token, based on the CURVE-ENDs&#39;relative location and orientation. If no segments are clearly aligned, a “beam-search,” as will be described in FIG. 30, may be performed. 
     Step  308  selects which among the PRIMITIVE-CURVE-FRAGMENTs, if any, is clearly aligned with the current PRIMITIVE-CURVE-FRAGMENT token, and step  310  makes the aligning PRIMITIVE-CURVE-FRAGMENT the current curve segment, and the aligning CURVE-END the new current starting end, and returns to step  300  to continue tracing. In FIG. 29, PRIMITIVE-CURVE-FRAGMENT  319  aligns with PRIMITIVE-CURVE-FRAGMENT  312 , PRIMITIVE-CURVE-FRAGMENT  319  becomes the current curve segment, and CURVE-END  318  the new current starting end. 
     Tracing of broken curvilinear contours, such as tracing the corners of a polygon, makes use of the simple curve tracing algorithm above. However, when a contour segment has no clear continuation beyond its far end as determined in step  304 , then a beam-search is used to search the SSB for tokens sufficiently aligned with the current token for the path to make a jump. The flowchart of FIG. 30 shows this contour tracing in relation to FIGS. 31-33. 
     In step  304  of FIG. 28, its was determined that there were no segments clearly aligned with the current PRIMITIVE-CURVE-FRAGMENT. For example, at segment  332  of FIG. 31, the far end from NBB  330  has no clearly aligned segment in its immediate vicinity. Likewise, segment  334  has no other clearly aligned segment in the vicinity of its lower end. 
     Step  320  then projects a segment from the CURRENT-FAR-END in the alignment of the current PRIMITIVE-CURVE-FRAGMENT, and then, in step  322 , projects a “flashlight beam” pattern from the CURRENT-FAR-END and encompassing the projected segment. In FIG. 32, beam projection  336  is projected around projected segment  335 , from the CURRENT-FAR-END of segment  332 . Similarly beam projection  340  is projected around projected segment  339  aligned with the CURRENT-FAR-END of segment  334 . 
     In step  324 , CURVE-END tokens within the projected area are located. As shown in FIG. 32, the CURVE-END of segment  338  will be encountered by beam projection  336 . Beam projection  340  will encounter several CURVE-END segments in its path—segments  341 - 344  all fall within the beam projection. 
     Step  326  checks the located tokens for sufficient alignment. In the case of beam projection  340 , segment  341  is clearly not in the proper orientation to align with segment  334 . Similarly, segment  343  would not be a candidate for since the end  343 a of the segment  343  closed to segment  334  is already attached to another segment  337 . Both segments  342  and  344  are properly oriented, however, segment  342  provides a better alignment with segment  334  than does segment  344 . Step  338  selects the best aligned PRIMITIVE-CURVE-FRAGMENT, and makes the new segment the current PRIMITIVE-CURVE-FRAGMENT for continued tracing. 
     FIG. 33 shows the resultant NBB  350  and its associated spatial subregion, represented as polygon  348 , determined by beam searching as described above. A second spatial subregion  352 , associated with another NBB  354 , may also be found using beam searching. 
     Tracing of branching contours, shown in the flowchart of FIG. 34, involves a simple modification of the simple contour tracing algorithm. In step  360  the CURRENT-FAR-END of the current PRIMITIVE-CURVE-FRAGMENT is identified. Step  362  locates CURVE-ENDs belonging to other PRIMITIVE-CURVE-FRAGMENTs near the CURRENT-FAR-END. Step  364  checks for suitably near and oriented CURVE-END tokens forming a branch off the current traced path, and a new independent trace of each branch is spawned in step  366  for however many are encountered, following the tracing steps as described in relation to FIG.  28 . Step  338  continues tracing along the current traced path. 
     Such methods of tracing leads to the identification of locations in the image which are the beginning of a line of text. Then, text segmentation demarcates the region of the image containing the text. A method for segmenting text is described in U.S. patent application Ser. No. (D/94267Q), previously referenced. From this segmentation it is an easy matter to extract the corresponding region of the original bitmap image to send to a separate handwriting recognition module for recognition of the text. 
     F. Miscellaneous 
     Although the invention has been described in relation to various implementations, together with modifications, variations and extensions thereof, other implementations, modifications, variations and extensions are within the scope of the invention. The invention is therefore not limited by the description contained herein or by the drawings, but only by the claims.