Abstract:
In one example, a method includes receiving, by a server and from a plurality of computing devices, data representative of a group of gesture-shortcut pairs that each include a gesture having been detected by at least one computing device from the plurality of computing devices and a shortcut associated with the detected gesture, and wherein the shortcut corresponds to an operation to be executed by the at least one from the plurality of computing devices. The method includes sorting the data representative of each respective gesture-shortcut pair from the group of gesture-shortcut pairs into at least two subgroups, a first subgroup including the first gesture-shortcut pair and a second including the second gesture-shortcut pair, the sorting being based on detected similarities between at least one of each respective gesture from the group of gesture-shortcut pairs and each respective shortcut from the group of gesture-shortcut pairs.

Description:
This application is a continuation of U.S. application Ser. No. 13/237,862, filed Sep. 20, 2011, the entire content of which is incorporated herein by reference. 
    
    
     BACKGROUND 
     Some known computing devices may include various hardware and software elements that may enable performance of a variety of tasks in response to user input. Conventionally, a user may interact with or otherwise activate the hardware and software elements through one or more interfaces (such as a keyboard, a mouse, a touch screen, etc.) by selecting one or more pre-configured graphical and/or hardware buttons (such as an icon, a switch, etc.). 
     For example, a user may use an Internet-enabled mobile device that browses the World Wide Web. The user may manually enter a Uniform Resource Locator (URL) for a desired webpage using a virtual keyboard or similar input device of the mobile device. The process of entering the URL may be complicated by the size or configuration of the input device. More specifically, the utility of virtual keyboards and other input devices designed for use with a mobile device (e.g., a smartphone) is often limited by the relatively small physical dimensions of the mobile device itself, negatively impacting the user experience. 
     One conventional solution is to implement gesture-based shortcuts that may be used to control a mobile device. A gesture (such as a pattern traced by a fingertip on a touch screen or other presence-sensitive device) may be detected by a mobile device. The detected gesture may be identified by the device and matched to one or more predefined shortcuts corresponding to one or more actions and/or operations performed by the device. In some instances, a computing device may graphically present to the user a list of the shortcuts associated with the detected gesture for selection by the user. Based at least in part on a received user selection, the computing device may then perform the one or more actions/operations corresponding to the selected shortcut (e.g., open a web browser application and connect to a specific location, execute a program, etc.). 
     In some instances, however, a user may not be willing to spend much effort to associate the gestures and shortcuts necessary to define a custom, user-specific gesture-based language. Further, the more gestures and shortcuts exist, the less likely it may be that the user will be able to recall the various custom-defined gestures and associated shortcuts. In other words, user-defined gesture-based languages may not scale. As the user-defined gesture-based language develops, the number of gesture and shortcut associations may reach the thousands, hundreds of thousands, or more such that the user may not be able to recall the various gestures and associated shortcuts of the gesture-based language. When a user is unable to recall the various gesture and associated shortcuts, the user may not use or otherwise abandon the gesture-based language and, instead, rely upon the keyboard for manual input. 
     SUMMARY 
     In one example of the disclosure, a method includes receiving, by a computing device, data representative of a gesture detected by a presence-sensitive screen of the computing device, identifying, by the computing device, a shortcut associated with the gesture, and providing for display, by the computing device, data representative of the shortcut. The shortcut corresponds to an action to be performed by the computing device. Identifying the shortcut comprises accessing at least a portion of an aggregated group of gesture-shortcut associations, the portion of the aggregated group having been determined based at least in part upon prior user input from at least one other user. 
     In another example of the disclosure, a method includes receiving, by a server and from a plurality of computing devices, data representative of a group of gesture-shortcut pairs, a first gesture-shortcut pair of the plurality of gesture-shortcut pairs being different from a second gesture-shortcut pair of the plurality of gesture-shortcut pairs, wherein each gesture-shortcut pair from the group of gesture-shortcut pairs includes a respective gesture having been detected by at least one computing device from the plurality of computing devices and a respective shortcut associated with the respective gesture, and wherein the respective shortcut corresponds to an operation to be executed by the at least one computing device from the plurality of computing devices. The method further includes sorting, by the server, the data representative of each respective gesture-shortcut pair from the group of gesture-shortcut pairs into at least two subgroups, a first subgroup from the at least two subgroups including the first gesture-shortcut pair and a second subgroup from the at least two subgroups including the second gesture-shortcut pair, the sorting being based at least in part on detected similarities between at least one of 1) each respective gesture from the group of gesture-shortcut pairs and 2) each respective shortcut from the group of gesture-shortcut pairs. 
     In another example of the disclosure, a computer-readable storage medium is encoded with instructions that, when executed, cause one or more processors of a computing device to perform operations, the operations including receiving, based on input of a first user, data representative of a gesture detected by a presence-sensitive screen of the computing device. The operations further include identifying a first shortcut associated with the gesture based at least in part on detected similarities between the gesture and graphical representations of at least a portion of an aggregated group of graphical representation-shortcut associations, the aggregated group of graphical representation-shortcut associations having been determined based at least in part on prior user input from at least a second user. The operations further include identifying a second shortcut associated with the gesture, wherein identifying the second shortcut includes using at least one handwriting recognition operation to identify at least one letter associated with the gesture, and identifying the second shortcut by at least accessing at least a portion of an aggregated group of letter-shortcut associations determined based upon prior user input from at least a third user. The operations further include selecting one of the first shortcut and the second shortcut as an identified shortcut, wherein the identified shortcut corresponds to an action to be performed by the computing device, and providing for display data representative of the identified shortcut. 
     In another example of the disclosure, a device comprises at least one processor, a network interface, and a language development module. The network interface is configured to receive data representative of a group of gestures, each gesture from the group of gestures having been detected by at least one computing device from a plurality of computing devices, receive data representative of one or more shortcuts, each shortcut from the one or more shortcuts being associated with at least one gesture from the group of gestures, wherein each of the one or more shortcuts corresponds to an operation to be executed by at least one of the plurality of computing devices. The language development module operable by the at least one processor to sort the data representative of the group of gestures and the data representative of the associated shortcuts received from the plurality of computing devices into a plurality gesture-shortcut groups, the sorting being based at least in part on detected similarities between at least one of 1) each gesture from the group of gestures and 2) each shortcut from the one or more shortcuts, wherein each gesture-shortcut group from the plurality of gesture-shortcut groups includes either a) data representative of a group of gesture-shortcut pairs sorted based at least in part on detected similarities between each gesture included in the respective gesture-shortcut group or b) data representative of a group of gesture-shortcut pairs sorted based at least in part on detected similarities between each shortcut included in the respective gesture-shortcut group. 
     The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating an example system for collaborative development of a gesture language for control of a computing device, in accordance with one or more aspects of the present disclosure. 
         FIG. 2  is a block diagram illustrating an example computing device that utilizes gestures for control of the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIG. 3  is a block diagram illustrating an example computing device that utilizes gestures for control of computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIG. 4  is a block diagram illustrating example server that utilizes and develops a collaborative gesture language for control of computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIG. 5  is a flowchart illustrating an example process for collaboratively developing a gesture language for control of the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIG. 6  is a flowchart illustrating an example process for processing an image of gesture, in accordance with one or more aspects of the present disclosure. 
         FIG. 7  is a flow chart illustrating an example process for analyzing a gesture detected by the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIGS. 8A-8D  are diagrams illustrating a text-based query for an example gesture-based application installed on the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIGS. 9A-9D  are diagrams illustrating a gesture-based query for an example gesture-based application installed on the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIGS. 10A-10D  are diagrams illustrating a gesture-based query followed by a text-based query for an example gesture-based application installed on the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
         FIGS. 11A-11D  are diagrams illustrating shortcut editing in an example gesture-based application installed on the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In general, this disclosure describes techniques for defining a gesture-based language based at least in part on aggregated input of a plurality of users that may be used to control a computing device. In some embodiments, a gesture-based language that associates particular gestures to shortcuts may be defined by one or more computing devices based at least in part on user input from any number of users (e.g., a user population). In some examples, the user population may include one or more users of computing devices, each of whom performs input operation (e.g., gestures) based at least in part on the gesture-based language such that the computing device is directed to perform an action (e.g., executing a program or initiating one or more other operations). The one or more other operations may include, for example, altering settings on the computing device (e.g., volume control, screen brightness, and on/off), accessing a list of contacts, controlling music playing software, opening a particular document, etc. 
     Techniques of this disclosure employ a gesture language development process based at least in part on aggregated user information designed to improve the scalability of gesture-based language development. In some embodiments, shortcuts defined by one or more members of a user population can be aggregated to facilitate the definition of an initial set of gestures and associated shortcuts for each individual member of the user population. For example, rather than requiring an individual user to define an initial set of gesture-shortcut associations, certain aspects of this disclosure may enable an individual user to use an initial library of gesture-shortcut associations included in a gesture-based language developed using aggregated user input from other users. Further, techniques of this disclosure may enable the user to enter user input configured to define and/or customize the initial library of gesture-shortcut associations based at least in part on continued use of the computing device. In this manner, a user may utilize an initial library of gesture-shortcut associations that is formed based at least in part on aggregated user input of the user population while maintaining the ability for the user to create custom gesture-shortcut associations. 
       FIG. 1  is a block diagram illustrating an example system for collaborative development of a gesture language for control of computing devices  2 . Server  12  may communicate with a plurality of computing devices  2 A- 2 N (collectively, “computing devices  2 ”) via network  10 . 
     Computing devices  2  represent a plurality of computing devices controlled by a plurality of users. The users of the plurality of computing devices  2  are collectively referred to as the user population. Computing devices  2  may detect one or more gestures  8 A- 8 N (collectively, “gestures  8 ”) using a presence sensitive surface of the computing device (e.g., presence sensitive display  4 A of computing device  2 A). Computing devices  2  may analyze gestures  8  locally on the computing devices, transmit data or an image representative of gestures  8  to server  12  for analysis, or both analyze gestures  8  locally and transmit data to server  12  for analysis. Computing devices  2  may transmit data representative of an action performed by the computing device specified and a gesture detected by the computing device to server  12 , contributing usage data to a database aggregating gesture-shortcut associations to develop a gesture-based language. The action performed by the computing device is represented by a shortcut and one or more gestures may be associated with the shortcut and one or more shortcuts may be associated with a gesture. 
     Computing devices  2  are coupled to server  12  through network  10  via a wired connection, wireless links, or both. Network  10  may include a telephone network (such as a cellular telephone network), a wide-area network (such as the Internet), a local-area network (LAN), an enterprise network, or one or more other types of networks). Computing devices  2  may transmit and receive data using network  10 . In some examples, network  10  may include one or more different networks. For instance, computing devices  2  may communicate with a private or public network (e.g., the Internet) via a cellular telephone network data connection or primary wireless radio connection and computing devices  2  and server  12  may communicate using a LAN or a public network, such as the Internet, via secondary cellular or wireless radio channel. 
     Server  12  may be configured to analyze a gesture detected by computing devices  2 , for example using Gesture Recognition Module (GRM)  16 . Computing devices  2  may transmit data or images representative of gestures  8  to server  12  via network  10 . GRM  16  of server  12  may compare the image or data representative of gestures  8  to images or data representative of known gestures stored in database  14  of server  12 . In some examples, GRM  16  may use a visual based analysis system to decompose and identify the image of the detected gesture. To accelerate comparison with known gestures, images or data representative of known gestures may be arranged in a clustered hierarchy. 
     Known gestures sharing similar characteristics or components may be grouped together in a cluster. For example, gestures including a significant horizontal stroke through the center of mass of the gesture and a second stroke oriented vertically to one side of the center of mass may be clustered together based at least in part on the shared characteristics. Clusters of known gestures sharing some similar traits may be grouped together in parent clusters. For example multiple clusters, each cluster sharing the trait of a significant horizontal stroke through the center of mass, may be formed into a parent cluster. Further, a ‘grandparent’ cluster may be formed from all parent clusters having the trait of a significant horizontal stroke, and so on. 
     Comparing data or an image representative of a gesture  8  (e.g., gesture  8 A) with known gestures to identify gesture  8 A does not require comparing gesture  8 A with every known gesture. Instead, comparisons may be made by progressing through the hierarchy of clustered gestures. In one example, the identification and association of gesture  8 A to one or more shortcuts may include comparing data or images representative of gesture  8 A to high level clusters, determining which parent cluster, if any, gesture  8 A shares characteristics with (e.g., a significant horizontal stroke from the previous example). Gesture  8 A may then be compared to the child clusters of the matched parent cluster, further identifying clusters of gestures that share characteristics with gesture  8 A. 
     The identification process may continue progressing through the hierarchy of parent-child clusters until the known gestures that form one or more child clusters are reached and compared. In examples where gesture  8 A is not identifiable as a known gesture, gesture  8 A may be added into a cluster, provided there is a sufficient degree of similarity with the existing known gestures in the cluster. Alternatively or in addition, a new cluster may be created using gesture  8 A. The new cluster may be independent of the existing clusters when gesture  8 A differs from the known gesture clusters or the new cluster may be created as a new child cluster within the existing hierarchy of clusters. In various instances, gesture  8 A is considered to differ from the known gesture clusters when a measure of difference between the features of the detected gesture (e.g., one or more of the distance regions of an image of the detected gesture shift to match with the known gesture, stroke number and/or length, and stroke orientation) and the known clusters exceeds a threshold amount. 
     Server  12  may be configured to store data or images representative of known gestures and associations between the known gestures and various shortcuts in a data repository (e.g., database  14 ). Database  14  may store a cluster hierarchy of known gestures, as developed by, for example, GRM  16 . Database  14  may also be configured to store the usage history of gestures  8 . The usage history may include data representative of gestures  8 , frequency of use of each gesture  8 , amount of time elapsed since last use of each gesture  8 , historical frequency of use trends, and user population defined gesture-shortcut associations. In some examples, server  12  may store the definitions, the gesture-shortcut associations, of a gesture-based language in database  14 . Access to the gesture-based language may enable GRM  16  to identify shortcuts associated with gestures  8  and transmit the identified shortcuts to computing devices  2  for presentation to the user. 
     Server  12  may be configured to define a gesture-based language for the user population using associated gestures and shortcuts aggregated from computing devices  2  of the user population. Computing devices  2  may transmit, to server  12  via network  10 , shortcuts selected by the user population as corresponding to gestures  8 . Language Development Module (LDM)  56  may aggregate gestures  8  detected on computing devices  2  and associate a gesture  8  with a shortcut selected by the user population. A selected shortcut may include a shortcut selected by the users of computing devices  2  as representing the desired action of computing devices  2  after inputting one or more gestures  8  on computing devices  2 . LDM  18  may analyze the usage history of the detected gesture-shortcut associations to define a language based at least in part on the collective use of the detected gestures. 
     When associating gestures and shortcuts to form a gesture-based language, LDM  18  may account for factors including usage frequency and usage frequency over time. For example, gesture  8 A may be repeatedly used by the user population to access the webpage “www.nytimes.com”. LDM  64 , based at least in part on the frequency of use, may define gesture  8 A as corresponding to the action of navigating a web browser to “www.nytimes.com”. Over time, the user population may no longer frequently use gesture  8 A to navigate to “www.nytimes.com” and, instead, use gesture  8 A to navigate to “www.nypost.com”. LDM  64  may, because of the changing usage of gesture  8 A, redefine gesture  8 A to correspond to the action of navigating a web browser to “www.nypost.com”. LDM  18  may assign multiple shortcut actions to a single a gesture, for example causing gesture  8 A to correspond to both “www.nytimes.com” and “www.nypost.com” given sufficient use of both associations by the user population. LDM  18  may assign multiple gestures to the same shortcut action (e.g., by associating both gesture  8 A and gesture  8 B with www.nytimes.com). 
     Server  12  may distribute the collaboratively developed gesture-based language and the corresponding clustered gesture hierarchy to computing devices  2  of the user population. Server  12  may distribute all or a portion of the gesture-based language. The distributed gesture-based language may be locally cached by a computing device  2 A and may include a subset of all gestures, and gesture-shortcut associations, forming the gesture-based language. Server  12  may select gestures and gesture-shortcut associations for distribution based at least in part on popularity (e.g., frequency of use by the user population), by local use (e.g., selecting gestures and gesture-shortcut associations corresponding to activities frequently performed by a particular computing device), or other standards. Computing devices  2  may utilize the distributed gesture-based language to locally identify gesture-shortcut associations that may have been used by the user population but not by an individual user of the computing device. In some examples, computing devices  2  may transmit locally created associations between gestures and shortcuts to server  12 . Server  12  may store these locally created associations, for example in database  14 . Computing devices  2  may use the copies of locally created associations stored on server  12  as backup copies of the associations or to assist a user in device migration. The server-stored copies of the association may enable a user to reload a customized gesture library onto a new or different computing device. 
     In some examples, computing devices  2  or server  12  may employ handwriting recognition techniques in combination with, or to supplement, the gesture-based language. Computing devices  2  and server  12  may treat handwriting detected on computing devices  2  as another arbitrary gesture. This approach may cause server  12  and computing devices  2  to define large numbers of similar gestures corresponding to each shortcut for which handwriting is used. For example, due to variations in handwriting, separate gestures each corresponding to the letter “M” may be created for Maps, Music, www.msn.com, and so on. Recognizing the image of “M” as the letter “M” may allow server  12  and/or computing device  2  to define fewer gestures, which may reduce the amount of memory used to identify and store the gestures as well as decrease the amount of time required to identify the appropriate shortcut, for example, by displaying partial matches as text is written on presence sensitive screen  4  of computing devices  2 . 
     Handwriting may be recognized based at least in part on the image of the handwritten letters and/or the component stroke fragments that form the image of the letters. Computing devices  2  or server  12  may segment characters detected on presence sensitive surface  4  of computing devices  2 , breaking the image of the handwriting apart into a series of stroke fragments. For example, computing devices  2  or server  12  may segment the text strokes about the local minima and maxima of the strokes on the y-axis of the image of the detected handwriting gesture. GRM  16  of server  12  or GRM  42  ( FIG. 3 ) of computing device  2 A may be configured to analyze the stroke fragments, in various instances, by applying a character recognizer to groups of stroke fragments and measuring how closely the different possible groupings of stroke fragments represent the images of letters in order to identify the letter handwritten letter. 
     Server  12  or computing devices  2  may perform handwriting and gesture analysis simultaneously or sequentially. In some examples, a handwriting sample may be unrecognizable as text to GRM  16  or  42  and the handwriting analysis may fail to reach a result or may produce an incorrect interpretation of the image. Gesture analysis, based at least in part on the image of previously detected handwriting gestures rather than the pattern of stroke fragments forming the letters of the handwriting gesture, may successfully identify the detected handwriting gesture and enable computing devices  2  or server  12  to identify the appropriate corresponding shortcut. 
     In some examples, both handwriting recognition and gesture analysis may be performed on the same detected gesture (e.g., gesture  8 A). In these examples, the handwriting recognition and gesture analysis may identify different shortcuts associated with the same detected gesture. Each identified shortcut may be assigned a rank upon being identified using handwriting recognition and gesture analysis. The rank may be based at least in part on various factors including the source of the identification (e.g., being identified by the computing device  2 A by matching a user-defined gesture-shortcut association stored within computing device  2 A versus being identified by server  12  using a letter-shortcut association defined by other users). The identified shortcut having the highest rank may be selected as the identified shortcut and output to the user. 
     Combining the handwriting recognition techniques with the gesture analysis techniques may provide certain advantages. For example, combining the information from the two sources may improve prediction accuracy. When detecting a gesture, computing devices  2  and/or server  12  may store a visual template of the gesture as well as perform handwriting recognition on the detected gesture. The next time a user draws the same textual string, computing devices  2  and/or server  12  may rely on the stored visual template of the gesture to correctly identify the associated shortcut even if the new gesture is too messy to be recognized correctly as handwriting. 
       FIG. 2  is a block diagram illustrating an example computing device  2 A that utilizes gestures for control of the computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. Computing device  2 A may communicate with server  12  via network  10 . 
     Examples of computing device  2 A may include, but are not limited to, portable or mobile devices (such as cellular phones, personal digital assistants (PDAs), portable gaming devices, portable media players, e-book readers, etc.) as well as non-portable devices (such as desktop computers, etc.). A gesture (e.g., gesture  8 A) may include, but is not limited to, a path traced by a fingertip (e.g., from position  6 A to position  6 B), stylus, or similar object on presence sensitive input device  4 A, such as the touch screen of a smartphone. Gesture  8 A may include a single or multiple strokes and need not form a single continuous path. In some examples, a software routine may render gesture  8 A scale and rotationally independent, so that the size or orientation of the drawing of gesture  8 A does not affect identification of the gesture. 
     Computing device  2 A is coupled to network  10  via a wired connection, wireless links, or both. Computing device  2 A may transmit and receive data over network  10 . Computing device  2 A may include multiple connections to network  10  (e.g., maintaining both a cellular data and wireless radio internet connection). Computing device  2 A may communicate with server  12  over network  10 . Network  10  may include one or more forms of electronic communication and use multiple channels of one or more types of electronic communication. 
     Computing device  2 A may detect the presence of gesture  8 A on presence sensitive interface  4 A. Presence sensitive interface  4 A may, in some examples, be a touch screen, track pad, or similar device. User  9  may draw gesture  8 A on the surface of computing device  2 A. Computing device  2 A may store an image of gesture  8 A and record data representative of gesture  8 A (such as the beginning and ending points of one or more strokes that form gesture  8 A) and images of features (such as the vertical, horizontal, diagonal, or other strokes that may form gesture  8 A) of gesture  8 A. 
     Computing device  2 A may identify gesture  8 A by comparing gesture  8 A to a database of known gestures stored locally on computing device  2 A. The database of known gestures may be arranged in a clustered hierarchy to reduce the amount of time required to identify gesture  8 A. In some examples, computing device  2 A may transmit an image or data representative of gesture  8 A to server  12  via network  10  for identification of gesture  8 A by server  12  (e.g., via GRM  16  of server  12 ). Gesture identification may also be performed by computing device  2 A in parallel with server  12 . For example, computing device  2 A may identify gesture  8 A using a locally stored gesture-based language while server  12  also performs the identification using a complete gesture-based language. Differences in identification of the gesture and the association of the shortcut with gesture  8 A may be resolved by selection by the user of the desired shortcut or, as another example, based at least in part on the amount error in the match between gesture  8 A and the known gesture. 
     Computing device  2 A may apply a gesture-based language to an identified gesture  8 A to identify one or more shortcuts corresponding to gesture. The gesture-based language, or a plurality of associated gestures and shortcuts forming a subset of the language, may be received from server  12  and stored locally on computing device  2 A. The gesture language may, for example, be stored in computing device  2 A as a database, lookup table, or index, where computing device  2 A accesses the entry of the identified gesture to retrieve the associated shortcuts. Computing device  2 A may also retrieve other known gestures associated with the retrieved shortcuts, enabling computing device  2 A to display alternative gestures to access a particular shortcut. In some examples, determination of one or more shortcuts associated with identified gesture  8 A may occur on server  12 , either alternatively to or in parallel with identification taking place on computing device  2 A. Server  12  may transmit an associated shortcut, or data representative of the associated shortcut, identified on server  12  to computing device  2 A for presentation to a user of computing device  2 A. 
     Computing device  2 A may display one or more shortcuts (e.g., shortcuts  10  and  12 ) associated with identified gesture  8 A for selection by a user (e.g., user  9 ). The shortcuts may be displayed in any order, including an order based at least in part on the frequency of the association of gesture  8 A and the shortcut, locally set preferences (e.g., a user of computing device  2 A routinely associates gesture  8 A with the shortcut), and various other characteristics of the gesture-shortcut association (e.g., alphabetization). For example, shortcut  24  may be typically mapped with gesture  8 A by the user of computing device  2 A. As the user of computing device  2 A repeatedly associates identified gesture  8 A with shortcut  24 , it may be likely that the user intends to access shortcut  24  by inputting gesture  8 A on the presence sensitive device  4  of computing device  2 A and that shortcut  24  should be displayed relatively early in the results list of shortcuts. Shortcut  22  may be typically associated with identified gesture  8 A by the user population. The gesture-based language, as determined by LDM  18  of server  12 , may map gesture  8 A with shortcut  22 , which may cause computing device  2 A to display shortcut  22  early in a list of shortcuts to be selected by the user. In this example, as the user personalized an association between gesture  8 A and shortcut  24 , the association between gesture  8 A and shortcut  22 , which was defined based at least in part on the user population, may be displayed after shortcut  24  in the shortcut result list. 
     A user may select the desired shortcut from a list of shortcuts presented by computing device  2 A to the user (e.g., using presence sensitive screen  4 A). Upon selection by the user, computing device  2 A may perform the action specified by the shortcut, for example opening a web browser to a specified address or activating a program. Computing device  2 A may record the selection of the shortcut, creating a locally stored association of gesture  8 A with the selected shortcut. Computing device  2 A may transmit data indicating that the user selected shortcut in response to gesture  8 A to server  12 . Server  12  may update usage history stored in database  14  and, aggregated over the user population, use the association data transmitted to server  12  by computing devices  2  to continue to develop the gesture language by, for example, updating gesture-shortcut associations and adding new gestures or shortcuts based at least in part on the aggregated usage patterns. Computing device  2 A may form the associations between gestures and shortcuts made by the user in a user customized dictionary that supplements the gesture language developed collaboratively by the user population on server  12 . 
     In some examples, computing device  2 A may display a gesture associated with a particular shortcut as defined by the collaboratively gesture-based language. For example, both shortcuts  10  and  12  are shown with a gesture similar but not identical to gesture  8 A. The gestures shown with shortcuts  10  and  12  may be gestures commonly associated with the shortcuts by the user population and mapped to the shortcuts in the gesture-based language. Displaying gestures associated with the shortcut by the gesture-based language facilitates a user learning the gesture language through the normal course of interaction with computing device  2 A. 
     In some examples, computing device  2 A may also display text entry field  20 , enabling a user to manually enter an address or shortcut (e.g., using a virtual keyboard). Manually entering the desired shortcut may be appropriate if, for example, the user was unsure of the gesture mapped to a desired shortcut or the desired shortcut was not displayed after inputting gesture  8 A into computing device  2 A. Computing device  2 A may display one or more shortcuts associated with the manually entered shortcut or allow the user to specify a customized gesture to associate with the shortcut. 
       FIG. 3  is a block diagram illustrating an example computing device  2 A that utilizes gestures for control of computing device shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure.  FIG. 3  illustrates only one particular example of client device  2 A, and many other example embodiments of client device  2 A may be used in other instances. 
     As shown in  FIG. 3 , computing device  2 A includes Gesture Recognition Module (GRM)  42 , operating system  40 , one or more processors  26 , memory  28 , a network interface  30 , one or more storage devices  32 , input device  34 , output device  36 , and power source  38 . Operating system  40  and GRM  42  are executable by one or more components of computing device  2 A. Each of components  26 ,  28 ,  30 ,  32 ,  34 ,  36 , and  38  may be interconnected (physically, communicatively, and/or operatively) for inter-component communications. In some examples, one or more of modules  42 ,  44 ,  46 ,  48 , and  50  may be part of the same module. In some examples, one or more of modules  42 ,  44 ,  46 ,  48 , and  50 , and one or more processors  26  may be formed in a common hardware unit. In certain examples, one or more of modules  42 ,  44 ,  46 ,  48 , and  50  may be software and/or firmware units that are executed on or operable by one or more processors  26 . 
     Processors  26 , in one example, are configured to implement functionality and/or process instructions for execution within computing device  2 A. For example, processors  26  may be capable of processing instructions stored in memory  28  or instructions stored on storage devices  32 . Such instructions may include components of operating system  40 , GRM  42 , or one or more modules of GRM  42 . 
     Memory  28 , in one example, is configured to store information within computing device  2 A during operation. Memory  28 , in some examples, is described as a computer-readable storage medium. In some examples, memory  28  is a temporary memory, meaning that a primary purpose of memory  28  is not long-term storage. Memory  28 , in some examples, is described as a volatile memory, meaning that memory  28  does not maintain stored contents when the computer is turned off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. In some examples, memory  28  is used to store program instructions for execution by processors  26 . Memory  28 , in one example, is used by software or applications running on computing device  2 A (e.g., operating system  40  and GRM  42 ) to temporarily store information during program execution. 
     Storage devices  32 , in some examples, also include one or more computer-readable storage media. Storage devices  32  may be configured to store larger amounts of information than memory  28 . Storage devices  32  may further be configured for long-term storage of information. In some examples, storage devices  32  include non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. 
     Computing device  2 A, in some examples, also includes a network interface  30 . Computing device  2 A, in one example, utilizes network interface  30  to communicate with external devices (e.g., server  12 ) via one or more networks, such as one or more wireless networks. Network interface  30  may be a network interface card (such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information). Other examples of such network interfaces may include Bluetooth®, 3G and WiFi® radios in mobile computing devices as well as USB. 
     Computing device  2 A also includes one or more input devices  34 . Input device  34 , in some examples, is configured to receive input from a user through touch or other gesture input. Examples of input device  34  include a presence-sensitive screen (e.g., presence-sensitive screen  4 A shown in  FIG. 2 ) such as touch-screen or track pad. One or more output devices  36  may also be included in computing device  2 A. Output device  36 , in some examples, is configured to provide output to a user using tactile, audio, or visual stimuli. Output device  36 , in one example, includes a presence-sensitive screen (e.g., presence-sensitive screen  4 A shown in  FIG. 2 ), sound card, a video graphics adapter card, or any other type of device for converting a signal into an appropriate form understandable to humans or machines. Additional examples of output device  36  include a speaker, a cathode ray tube (CRT) monitor, a liquid crystal display (LCD), or any other type of device that can generate intelligible output to a user. 
     Computing device  2 A, in some examples, may include one or more power sources  38 , which may be rechargeable and provide power to computing device  2 A. Power source  38 , in some examples, may include batteries made from nickel-cadmium, lithium-ion, or other suitable material. 
     Computing device  2 A may include operating system  40 . Operating system  40 , in some examples, controls the operation of components of computing device  2 A. For example, operating system  40 , in one example, facilitates the interaction of GRM  42  with processors  26 , memory  28 , network interface  30 , storage device  38 , input device  34 , and output device  36 . 
     Any applications or software modules (e.g., GRM  42 ) implemented within or executed by computing device  2 A may be implemented or contained within, operable by, executed by, and/or be operatively/communicatively coupled to components of computing device  2 A, such as processors  26 , memory  28 , network interface  30 , storage devices  32 , input device  34 , and/or output device  36 . 
     Computing device  2 A may use GRM  42  to identify gesture  8 A of  FIG. 2  detected by input device  34  (e.g., presence sensitive screen  4 A of computing device  2 A of  FIG. 2 ). GRM  42  may decompose an image of gesture  8 A into major features, prepare data or images representative of the features of gesture  8 A for comparison, and compare the data or images representative of gesture  8 A with known gestures. In some examples, GRM  42  may identify one or more shortcuts associated with the identified gesture and expand a database of known gestures and associations to include identified gesture  8 A. GRM  42  may include extraction module  44 , smoothing module  46 , recognition module  48 , and cluster module  50 . 
     Extraction module  44  of GRM  42  may spatially sample an image of gesture  8 A. Spatial sampling may include forming a matrix or other representation of the image of gesture  8 A using pixel values selected according to the position of the pixels within the image of gesture  8 A. For example, extraction module  44  may be configured to decompose an image of gesture  8 A into a series of feature images indicating the degree of orientation of a stroke forming gesture  8 A with a particular direction (e.g., measuring how close to horizontal, vertical or diagonal a stroke of gesture  8 A is) by spatially sampling over the image of gesture  8 A. Extraction module  44  may also be configured to scale, rotate, and translate gesture  8 A to compensate for variations in orientation, position, and size as gesture  8 A was drawn on computing device  2 A. For example, extraction module  44  may normalize gesture  8 A about the center of mass of gesture  8 A (center of mass may measure the center of the image of gesture  8 A, for example, via the average position of all pixels forming gesture  8 A, an intensity weighted average of the image of gesture  8 A, or another process of measure), set the origin of gesture  8 A to the center of mass, and scale gesture  8 A such that the image of gesture  8 A to a predefined width and height that corresponds with the size of images of known gestures. 
     Smoothing module  46  of GRM  42  may apply a smoothing function, such as a Gaussian smoothing function, to each image of the features of gesture  8 A. Smoothing the images may reduce the sensitivity of the identification of gesture  8 A to noise or small variations in the drawing of gesture  8 A. Smoothing module  46  may downsample the feature images. For example, smoothing module  46  may apply a filter that reduces the number of pixels in the downsampled image by representing a series of subsets of pixels of the smoothed image with a statistic, such as the mean or maximum. Smoothing module  46  may subdivide the smoothed image into squares of four contiguous pixels, where each pixel of the downsampled image is the maximum value of the corresponding four pixel square of the smoothed image. Downsampling may speed comparison by reducing the number of data points (pixels) to compare with known images. Downsampling may also further reduce the sensitivity of the gesture comparison to minor variations in drawing gesture  8 A. 
     Recognition module  48  may compare the feature images of gesture  8 A to feature images of known gestures to determine whether a known gesture corresponds to gesture  8 A. In some examples, recognition module  48  may apply a deformable template matching algorithm to the feature images of gesture  8 A. This may allow each point in the feature images of gesture  8 A to shift the known gesture feature image to form the best match. The range of the shift may be limited to speed comparison (e.g., allowing each point to shift a maximum of 3 pixels from the corresponding location on the feature images of the known gesture). To reduce overfitting, the shift may take local context into account by shifting and matching a patch surrounding a point rather than just the point. Agreement between the feature images of the detected gesture and known gesture may be represented by the image deformation model distance, computed as the sum of squared differences between the feature images at the respective patch locations. Upon identification of gesture  8 A, recognition module  48  may retrieve one or more shortcuts corresponding to gesture  8 A in a database stored in, for example, storage device  32 . The retrieved shortcuts may be presented to a user via output device  36 . 
     Cluster module  50  may accelerate the identification of gesture  8 A by reducing the number of known gestures that recognition module  48  compares with gesture  8 A. Known gestures may be arranged in a hierarchy, e.g., a parent-child clustered hierarchy. Cluster module  50  may select known gestures for comparison by recognition module  48  based at least in part on the distance between the feature images of gesture  8 A and the known images. Cluster module  50  may progress through the hierarchy, eliminating dissimilar clusters of known gestures from comparison based at least in part on the output of recognition module  48 . gesture  8 A may be identified once cluster module  50  proceeds through the cluster hierarchy such that there are no more child clusters available, only the known gestures which form the clusters. In some examples, cluster module  50  may add gesture  8 A to a cluster, provided that gesture  8 A is similar to the other gestures in the cluster but not dissimilar enough to warrant forming a new cluster, either independent of the current gesture clusters or as a child cluster of a current gesture cluster. Cluster module  50  may store the clustered hierarchy of known gestures in a database or similar file structure in storage device  32 . In some examples, cluster module  50  may also store shortcuts associated with the gestures by a user of computing device  2 A in the gesture entry in the clustered hierarchy or in a different index or database containing mapped gestures and shortcuts. 
       FIG. 4  is a block diagram illustrating example server  12  that utilizes and develops a collaborative gesture language for control of computing device  2 A shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure.  FIG. 4  illustrates only one particular example of server  12 , and many other example embodiments of server  12  may be used in other instances. 
     As shown in  FIG. 4 , server  12  may include Language Development Module (LDM)  56 , Gesture Recognition Module (GRM)  64 , operating system  63 , one or more processors  52 , memory  54 , a network interface  56 , and one or more storage devices  58 . Operating system  60  and GRM  16  are executable by one or more components of server  12 . Each of components  48 ,  50 ,  52 , and  54  may be interconnected (physically, communicatively, and/or operatively) for inter-component communications. 
     One or more processors  52  may include, in certain examples, any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry. Processors  52 , in one example, are configured to implement functionality and/or process instructions for execution within server  12 . For example, processors  52  may be capable of processing instructions stored in memory  54  or storage devices  58 . 
     Memory  54 , in one example, is configured to store information within server  12  during operation. Memory  54 , in some examples, is described as a computer-readable storage medium. In some examples, memory  54  is a temporary memory, meaning that a primary purpose of memory  54  is not long-term storage. Memory  54 , in some examples, is described as a volatile memory, meaning that memory  54  does not maintain stored contents when the computer is turned off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. In some examples, memory  54  is used to store program instructions for execution by processors  52 . Memory  54 , in one example, is used by software or applications running on server  12  (e.g., operating system  60 , LDM  18 , and GRM  16 ) to temporarily store information during program execution. 
     Storage devices  58 , in some examples, also include one or more computer-readable storage media. Storage devices  58  may be configured to store larger amounts of information than memory  54 . Storage devices  58  may further be configured for long-term storage of information. In some examples, storage devices  58  include non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage device  58  may include database  14  ( FIG. 1 ), that stores definitions of a gesture-based language developed by server  12  with data aggregated from the user population of computing devices  2 . 
     Server  12  also includes a network interface  56 . Server  12 , in one example, utilizes network interface  56  to communicate with multiple computing devices, e.g., computing device  2 A ( FIGS. 1 and 2 ), via one or more networks, e.g., network  10 . Network interface  56  may be a network interface card (such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information). 
     Server  12  may include operating system  60 . Operating system  60 , in some examples, controls the operation of the components of server  12 . For example, operating system  60  may coordinate the interaction of GRM  16 , LDM  18 , processor  52 , memory  54 , network interface  56 , and storage device  58 . Any software or hardware modules, e.g., operating system  60 , GRM  16 , and LDM  18 , implemented within or executed by server  12  may be implemented or contained within, operable by, executed by, and/or be operatively/communicatively coupled to components of server  12 , e.g., processors  52 , memory  54 , network interface  56 , storage devices  58 . 
     GRM  16  may include extraction module  62 , smoothing module  46 , recognition module  48 , and cluster module  64 . In some examples, server  12  may receive, via network interface  56 , an image of a detected gesture (e.g., gesture  8 A of  FIG. 1 ) from one of a plurality of computing devices, such as computing device  2 A of  FIG. 1 . Server  12  may call GRM  16  to identify the received gesture. Extraction module  62  may decompose the image of the received gesture into one or more feature images, images emphasizing various standard characteristics of a gesture. Feature images may include representations of the vertical, horizontal, or diagonal strokes contained within the image of the received gesture. The feature images of the received gesture may be passed to smoothing module  46  and recognition module  48  for further processing as described previously with respect to  FIG. 3 . In other examples, server  12  may receive preprocessed feature images of the detected gesture from computing device  2 A, allowing server  12  to bypass extraction module  62 , and, in some examples, smoothing module  46  and to proceed with identification by recognition module  48 . 
     Cluster module  64  may accelerate identification of the received gesture by progressing though a clustered hierarchy of known gestures. Cluster module  64  may retrieve representative feature images characteristic of known clusters of gestures within a particular cluster for comparison by recognition module  48 . As clusters of gestures are discarded as insufficient matches to the received gesture, cluster module  64  may progress down a parent child hierarchy into sub clusters of known gestures and eventually into providing feature images of known gestures themselves for comparison. As the distinction between known gestures becomes finer (e.g., the known gestures are grouped in similar or the same clusters) smoothing module  46  may reduce downsampling and recognition module  48  may perform the image comparison between the received and known gestures over more data points. This may increase accuracy at the expense of computation time. Cluster module  64  may store received gesture  8 A in the clustered hierarchy, for example, in a database or index maintained in storage device  58 . In some examples, cluster module  64  may remove disused gestures and gestures from the clustered gesture hierarchy. 
     Server  12  may include LDM  18 . LDM  18  may include Usage Pattern Module (UPM)  66  and mapping module  68 . LDM  18  may define a gesture language using gesture and shortcut data aggregated from the user population of computing devices  2 . Server  12  may identify gesture  8 A detected on computing device  2 A, via recognition module  48 , and return one or more shortcuts associated with the identified received gesture to computing device  2 A. Computing device  2 A may transmit to server  12  a shortcut selected by the user of computing device  2 A as associated with gesture  8 A. In other examples, server  12  may receive the identification of gesture  8 A, along with shortcut information associated with gesture  8 A, from computing device  2 A. LDM  18  may aggregate the usage data of gestures  8 A and user associated shortcuts in storage device  58 , analyze the usage data, and define gesture-shortcut mappings to form a gesture-based language. This gesture-based language may be stored as a database or index in storage device  58 . All or a portion of the gesture-shortcut mappings may be transmitted to computing devices  2 , enabling computing devices  2  to utilize the gesture language without connection to server  12 . 
     UPM  66  may analyze the pattern of use of gestures and shortcuts among the user population. Usage patterns may include frequency of an association between a particular gesture and shortcut, frequency of such an association over time, time since last association between a gesture and shortcut, number of times a gesture and shortcut are associated, and similar metrics. For example, UPM  66  may identify one or more shortcuts most frequently associated with a particular gesture by the user population. The gesture-based language may define that particular gesture as representing the one or more identified shortcuts. Over time, the usage of the particular gesture may shift, coming to be more frequently used to represent a different set of shortcuts. UPM  66  may identify the declining usage of the prior definition in the gesture-based language and redefine the gesture to reflect the more common new usage within the user population. Depending on usage, multiple gestures and shortcuts may be associated together. 
     Mapping module  68  may store one or more associations between a gesture and a shortcut in a database, e.g., database  14 , in storage device  58 . These associations may be determined by UPM  66  and form a gesture-based language for computing devices  2 . User customized associations between gestures and shortcuts may also be stored in database  14  or some other location in storage device  58 . For example, a user may create one or more gestures or associations between a gesture and a shortcut unique to the individual user. Computing device  2 A may transmit the customized gesture or gesture-shortcut association to server  12 . Server  12  may retain a copy of the customized gesture in the gesture clustered hierarchy and may store a copy of the unique gesture-shortcut association. The unique association may be used by recognition module  48  to identify a shortcut with the gesture or by server  12  to reload computing device  2 A with the customized associations of a user if necessary, for example during computing device migration. 
     Although shown as separate components in  FIG. 4 , in some examples, one or more of modules  16 ,  18 ,  46 ,  48 ,  62 ,  64 ,  66 , and  68  may be part of the same module. In some examples, one or more of modules  16 ,  18 ,  46 ,  48 ,  62 ,  64 ,  66 , and  68 , and one or more processors  52  may be formed in a common hardware unit. In certain examples, one or more of modules  16 ,  18 ,  46 ,  48 ,  62 ,  64 ,  66 , and  68  may be software and/or firmware units that are executed on or operable by one or more processors  52 . 
       FIG. 5  is a flowchart illustrating an example process for collaboratively developing a gesture language for control of computing device  2 A shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. For purposes of illustration only, the example process is described below within the context of server  12  of  FIGS. 1 and 4  and computing device  2 A of  FIGS. 1 and 3 . However, the example process of  FIG. 5  may be performed using other devices. 
     Server  12  may receive data representative of gestures detected by a plurality of computing devices  2  ( 500 ). Computing devices  2  of the user population may transmit images or data representative of gestures  8 A to server  12  for identification or gesture-based language development. Data representative of a detected gesture may include an image of the detected gesture or images of features of the detected gesture. For example, computing device  2 A may decompose an image of the detected gesture in feature images of the strokes of the gesture directed along various axes, such as a series of images showing the horizontal, vertical, and diagonal strokes that form the gesture. In other examples, computing device  2 A may locally identify the detected gesture and transmit an identification of the detected gesture for processing by LDM  18  of server  12 . The identification or images of the detected gesture may be cached in memory  54  of server  12  for further processing by one or more modules or components of server  12 . 
     Server  12  may receive data representative of one or more shortcuts associated with each of the gestures ( 502 ). In some examples, computing devices  2  may further transmit data representative of a shortcut selected by a user of a computing device  2 A after inputting or creating a gesture. Server  12 , through network interface  56  and network  10 , may receive this data and store the data in memory  54  for processing or place the data in long term storage in storage device  58 . Customized gestures and gesture-shortcut associations may be stored in storage device  58  in database  14  or some other form of index or database accessible by other components and modules of server  12 , such as GRM  16 , to be retrieved and used to identify gestures detected on the originating computing device or to allow a user of the originating computing device to restore or migrate the customized gesture-shortcut associations the user developed. 
     Server  12  may aggregate the data representative of the gestures and associated shortcuts as aggregated information ( 504 ). Aggregating the data may include identifying received gestures from one or more computing devices  2  of the user population with GRM  16 , associating these identified gestures with user selected shortcuts (received from computing devices  2 ), and storing the resultant associations. This history of association may be placed in long term memory, such as storage device  58 . The aggregated data may also include customized and locally identified gesture-shortcut associations received from the computing devices  2  of the user population. 
     UPM  66  of LDM  18  of server  12  may define a gesture-shortcut language based at least in part on the aggregated data ( 506 ) by analyzing the usage patterns of gesture-shortcut associations in aggregated data. The gesture-shortcut language may be based at least in part on a usage statistic, such as the frequency a particular gesture-shortcut association occurs in the data aggregated over the user population. A gesture frequently associated with a particular shortcut by the user population may be defined by UDM  58  as a gesture-shortcut association. In some examples, particular gesture may be frequently associated with multiple shortcuts by various segments of the user population. UDM  58  may store such a gesture with a plural definition, allowing the gesture to represent multiple shortcuts and allowing multiple shortcuts to be returned to a computing device  2 A when the particular gesture is identified for the computing device. 
     UDM  58  may also store multiple gestures representing the same shortcut. UPM  66  may track one or more usage statistics to develop the gesture-based language over time, adjusting the gesture-shortcut definitions as usage changes or new gestures or shortcuts are developed by the user population. As gesture-shortcut associations fall into disuse, UPM  66  may detect these outmoded gesture-shortcut associations by, for example, monitoring time elapsed since last association by the user population or monitoring the frequency of association over time and discard them. UPM  66  may also edit one or more databases of gesture cluster hierarchies, removing infrequently used gestures or gesture clusters, reducing memory requirements for storage and reducing identification times of received detected gestures in GRM  16  by avoiding comparisons to disused gestures. 
     Sever  12 , in a database (e.g., database  14 ) in storage device  58 , may store data representative of the mapped gesture and shortcut in a database ( 508 ) such as the gesture-shortcut associations. Mapping module  68  of LDM  18  may cause processor  52  to store the data representative of the gesture-shortcut association in database  14  accessible to GRM  16  to allow identification of one or more gestures  8 A received from computing devices  2 . Mapping module  68  and cluster module  64 , alone or in conjunction, may store data representative of a detected gesture in a gesture cluster hierarchy in storage device  58 . 
     Server  12  may propagate the data representative of the mapped gesture and shortcut to the plurality of users ( 510 ) by transmitting all or a subset of the gesture-shortcut associations forming the gesture-based language developed by LDM  18  of server  12  to computing devices  2  of the user population via network  10 . Server  12  may also transmit portions of the clustered gesture hierarchy corresponding to the transmitted gesture-based language to computing devices  2 . Computing devices  2  may utilize the transmitted gesture-based language and clustered hierarchy to perform identifications of detected gestures and retrieve shortcuts associated with the identified gestures locally on computing devices  2 . 
       FIG. 6  is a flowchart illustrating an example process for processing an image of gesture  8 A. For purposes of illustration only, the example process is described below within the context of GRM  16  of server  12  of  FIG. 4  and GRM  42  of computing device  2 A of  FIG. 3 . However, the example process of  FIG. 6  may be performed using other devices. 
     Extraction module  44  of GRM  42  of computing device  2 A ( FIG. 3 ) or extraction module  62  of GRM  16  of server  12  ( FIG. 4 ) may decompose an image of a detected gesture ( 600 ). Decomposing an image of a detected gesture may include processing the image in preparation for comparison and extracting one or more features of the gesture captured in the image. The initial image of gesture  8 A may be scaled to aid in comparison, for example stretching or shrinking the image so that the height and width of the detected gesture is one standard deviation in each direction. Another method to scale gesture  8 A is to use a bounding box, fitting the image into a set frame size. The image may also be translated, which may include positioning the image of the detected gesture around the center of mass of the strokes that form the detected gesture. 
     GRM  42  or  64  may resolve gesture  8 A into one or more feature images corresponding to the features of the strokes which form gesture  8 A, breaking the image of gesture  8 A apart such that each feature image shows the strokes in the image of gesture  8 A corresponding to a particular direction (e.g., horizontal, vertical, and diagonal). For example, a feature image may show all horizontal strokes in gesture  8 A, while another feature image shows all vertical strokes that compose gesture  8 A. A third and fourth feature image may show diagonal strokes of the detected gesture. A fifth feature image may represent the beginning and end points of the strokes that form gesture  8 A. Some gestures, such as the numbers “3” and “8”, have relatively similar shapes and strokes. The typical start and end points of the strokes that form the “3” and “8” are different. A “3” is typically drawn starting near the top and progressing down, finishing near the bottom of the symbol. A stroke forming an “8” is likely to both begin and end near the top of symbol. While the feature images of a “3” and “8” are likely to show similar horizontal, vertical, and diagonal strokes, the start and end points may provide sufficient distinction to differentiate the gestures. 
     Smoothing module  46  of GRM  42  and/or  64  may apply a smoothing function in order to smooth the feature images ( 602 ) by, for example, applying a Gaussian smoothing function to the feature images of gesture  8 A. Smoothing module  46  of GRM  42  and/or  64  may also downsample feature images ( 604 ) to further reduce the sensitivity of the feature images to noise. Downsampling the featured images may reduce the number of data points (pixels) in each of the featured images by computing a statistic (such as the min, max, mean, etc.), for a moving window of pixels. In one example, smoothing module  46  may apply a 3×3 moving window to each feature image. Each data point in the down sampled image may correspond to the maximum value of the pixels in the corresponding 3×3 section of the feature image. As GRM  42  or  64  compares gesture  8 A to increasingly similar known gestures, e.g., as the identification algorithm progresses through the clustered gesture hierarchy, downsampling may be reduced or eliminated, allowing fine features of a gesture obscured by downsampling to affect the identification of gesture  8 A. 
     Recognition module  48  of computing device  2 A or server  12  may compare feature images of a detected gesture to a known gesture ( 606 ), for example, by comparing feature images of gesture  8 A to feature images of a known gesture. Data representative of a known gesture, such as feature images, may be stored in storage device  58  of server  12  or storage device  32  of computing device  2 A. Recognition module  48  of GRM  42  or  64  may compute an image deformation model distance for the comparison of the detected and known gesture. Recognition module  48  may calculate the distance between a patch of the feature images and the best matching area of the known gesture feature images. Recognition module  48  may limit the search for the best match to the corresponding area of the known gesture feature image. For example, recognition module  48  may limit the match algorithm to an examination of the region on the known gesture feature image within a fixed distance of the location corresponding to the patch location on the detected gesture feature image. To determine the strength of the match between the known and detected gesture, recognition module  48  may sum, over the known gesture image, the sum of the squared differences between the detected and known gesture feature images at the matched patch locations. 
     Recognition module  48  may progress through a clustered gesture hierarchy, reducing the number of comparisons that are made to dissimilar known gestures as dissimilar gestures are eliminated before comparison due to their position in s discarded cluster. For example, a cluster that is found to be dissimilar from gesture  8 A by recognition module  48  may be removed from comparison, along with all of the children gestures and gesture clusters that form the discarded gesture cluster. Cluster module  64  or  46 , of server  12  and computing device  2 A respectively, may maintain the clustered gesture hierarchy in storage device  58  or  28 . 
       FIG. 7  is a flow chart illustrating an example process for analyzing a gesture detected by computing device  2 A shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. For purposes of illustration only, the example process is described below within the context of server  12  of  FIGS. 2 and 4  and computing device  2 A of  FIGS. 2 and 3 . However, the example process of  FIG. 7  may be performed using other devices. 
     Computing device  2 A may receive an event indicating detection of a gesture ( 700 ),e.g., detecting gesture  8 A ( FIG. 2 ) via input device  34  ( FIG. 3 ) of computing device  2 A. Input device  34 , for example presence sensitive screen  4 A ( FIG. 2 ), may store gesture  8 A in memory  28  of computing device  2 A ( FIG. 3 ) for processing. Gesture  8 A may be stored as an image and may be accompanied by associated data, such as the locations of stroke start and end points. Multiple strokes may form gesture  8 A and these strokes need not be continuous. 
     GRM  42  of computing device  2 A or GRM  16  of server  12  may identify a shortcut based at least in part upon the detected gesture ( 702 ) by comparing an image or data representative of a gesture  8 A to images or data representative of known gestures. GRM  42  may retrieve the image and associated data of gesture  8 A from memory  28  and process the image for identification. GRM  42 , using extraction module  44 , may decompose the image of gesture  8 A into a series of feature images representing the characteristics of the strokes that form gesture  8 A. Smoothing module  46  may apply a smoothing function to the feature images to reduce the effect of minor errors in inputting or capturing gesture  8 A. In some examples, one or more of these steps may be performed remotely from computing device  2 A on server  12 . Computing device  2 A may transmit data representative of gesture  8 A, such as an image or set of feature images of gesture  8 A, to server  12  for identification and may receive an identification of gesture  8 A and/or a one or more shortcuts represented by the identified gesture  8 A. 
     Recognition module  48  of computing device  2 A or server  12  may compare data representative of the detected gesture to a gesture cluster ( 704 ) by comparing the feature images of gesture  8 A to the feature images of a known gesture using recognition module  48 . Recognition module  48  may compare portions of the smoothed and downsampled feature images of the detected gesture with feature images of known gesture found in the clustered gesture hierarchy as discussed above with respect to  FIGS. 5 and 6 . In some examples, comparison may take place on computing device  2 A, e.g., using GRM  42 , while in other examples comparison may be performed on server  12  or at both locations in parallel. 
     Cluster module  50  of computing device  2 A ( FIG. 3 ) or cluster module  64  of server  12  ( FIG. 4 ) may select a predefined number of child gesture clusters related to the detected gesture ( 706 ). Cluster module  50  or  64  may first supply recognition module  48  with data representative of the gestures in the top-level, e.g., largest, clusters. Cluster module  50  or  64  and recognition module  48  may then select a predefined number of the closest child clusters of the top-level clusters for comparison. 
     Cluster module  50  or  64  may repeat the selection of children gesture clusters for each generation until only gestures are selected ( 708 ) continuing the selection and comparison cycle of step  706  until no gesture clusters are available for selection. As cluster module  50  or  64  and recognition module  48  progress through the clustered gesture hierarchy, eventually the cluster modules will be unable to retrieve further child cluster gestures. Cluster module  50  or  64  may retrieve data representative of the known gestures with the last cluster for comparison by recognition module  48  to identify gesture  8 A. 
     Computing device  2 A, e.g., through output device  36  ( FIG. 3 ), may generate the shortcut for display on the computing device ( 710 ), for example, output device  36  may display one or more shortcuts, such as shortcuts  10  and  12  ( FIG. 2 ). Output device  36  may include presence sensitive screen  4 A of computing device  2 A ( FIG. 2 ). The identified gesture  8 A may be associated with one more shortcuts through a gesture-based language. Definitions for the gesture-based language may be stored in one or more databases or indexes, such as database  14  of server  12 , that are remotely and/or locally accessible. In some examples, computing device  2 A may have a locally stored user customized list of gesture-shortcut associations. These gesture-shortcut associations may be independent of the gesture-based language. Computing device  2 A may display both shortcuts selected via the user customized list of gesture-shortcut associations and gesture-shortcut associations defined by the gesture-based language. In some examples, computing device  2 A may receive data representative of one or more associated shortcuts from a remote location, such as server  12 . 
     Computing device  2 A may display the shortcuts associated with the identified gesture  8 A in a list format, allowing a user of computing device  2 A to select the desired shortcut. Computing device  2 A may transmit information relating to the selection to server  12 , allowing server  12  to update usage history for the shortcut and gesture and contribute to the aggregated usage history data that LDM  18  of server  12  ( FIG. 4 ) may use to continue development of the gesture-based language. 
       FIGS. 8A-8D  are diagrams illustrating a text-based query to an example gesture-based application installed on computing device  2 A shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. A text-based query to the example gesture-based application may allow the user to retrieve and learn a gesture representing a desired shortcut. 
       FIG. 8A  shows computing device  2 A displaying a gesture-based application. If a user is unsure of the gesture associated with a desired action or shortcut, the user may enter a text based query into text entry field  20  of computing device  2 A to cause computing device  2 A to search for and display a desired shortcut and gesture. The user may select text entry field  20 , via by tapping or otherwise indicating the selection on presence sensitive screen  4 A of computing device  2 A, to cause computing device  2 A to activate text entry field  20 . 
       FIG. 8B  shows a partially entered search string in text entry field  20  of computing device  2 A, entered using virtual keyboard  72 . “Go” button  74 , when actuated (e.g., via tapping with a fingertip, stylus, or other method), commands computing device  2 A to search either an onboard database of gesture-shortcut associations for shortcuts matching the search string in text entry field  20  and/or transmit the query to server  12  to consult a gesture language database maintained externally to computing device  2 A. Computing device  2 A and/or server  12  may match the search string dynamically, as the string is entered (e.g., performing a search on the partially entered search string as the string is being inputted into text entry field  20 ). Computing device  2 A may display any partial matches from the user&#39;s browser history or prior usage of computing device  2 A in a list view below text entry field  20  (e.g., shortcuts  10  and  12 ). These shortcuts may be displayed along with gestures associated with the shortcuts, allowing the user to learn the gestures to simplify access to the various webpages and applications. 
       FIG. 8C  shows an example where the shortcut returned by the search does not have a gesture associated with the shortcut. In some examples, a particularly shortcut may not have a gesture associated with it, such as shortcut  76  to “news.google.com”. In other examples, a returned shortcut may have multiple gestures associated with the shortcut. In such instances computing device  2 A may display a single gesture for the shortcut, for example, the gesture most commonly associated with the shortcut. 
       FIG. 8D  shows how the user may select the correct shortcut from a list of results. Selecting shortcut  76 , by tapping or otherwise indicating the selection on presence sensitive screen  4 A of computing device  2 A, may cause computing device  2 A to launch a web browser to the selected shortcut location (here, opening a web browser to the website “www.google.com”) or otherwise perform the action indicated by the selected shortcut. 
       FIGS. 9A-9D  are diagrams illustrating a gesture-based query to an example gesture-based application installed on computing device  2 A shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. Drawing a gesture may allow a user to avoid using more cumbersome input mechanisms to computing device  2 A, such as soft keyboards. 
       FIG. 9A  shows computing device  2 A equipped with a presence sensitive screen  4 A. Computing device  2 A is showing a gesture-based application. A user may draw a gesture on presence sensitive screen  4 A to cause computing device  2 A or server  12  to search for one or more shortcuts associated with the gesture. 
       FIG. 9B  shows a user drawn gesture  8 A on presence sensitive screen  4 A of computing device  2 A instead of manually entering the desired shortcut in text entry field  20 . Computing device  2 A may analyze gesture  8 A using GRM  42  of computing device  2 A ( FIG. 2 ) as discussed above. Computing device  2 A may identify gesture  8 A and use a gesture-based language to determine one or more shortcuts associated with identified gesture  8 A. In some examples, gesture and/or shortcut identification may take place remotely from computing device  2 A on server  12 . 
       FIG. 9C  shows the gesture-based application of computing device  2 A displaying a list of shortcuts associated with gesture  8 A. Computing device  2 A may display the shortcuts of one or more gestures that most closely match the gesture  8 A in, for example, a list view below gesture  8 A. Some shortcuts may be associated with gesture  8 A based at least in part on the gesture-based language (e.g. shortcut  22 ) while other shortcuts may be associated with gesture  8 A through user customization of a gesture-shortcut relationship. Such a user customized association may be stored locally on computing device  2 A (e.g., in storage device  32 ) or externally on server  12 . 
       FIG. 9D  shows the selection of the desired shortcut  24  corresponding to the inputted gesture, gesture  8 A. The user may choose the desired shortcut from the list by tapping or otherwise indicating the selection, and computing device  2 A may perform the action indicated by shortcut  24 . Computing device  2 A may transmit data representative of the selection of the desired shortcut to server  12 , which may store the selection or update the usage history of the gesture and/or shortcut to reflect the selection. Server  12 , using LDM  18 , may continue to aggregate usage history data from across the user population to continue to develop the gesture-based language, propagating updated associations to computing devices  2 . 
       FIGS. 10A-10D  are diagrams illustrating a gesture-based query followed by a text-based query to an example gesture-based application installed on computing device  2 A shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. In some situations a desired shortcut is not associated with a particular gesture, so a user may create a customize association between the entered gesture and the desired shortcut. 
       FIG. 10A  shows a computing device  2 A equipped with a gesture-based interface and a gesture, gesture  8 A, inputted on presence sensitive screen  4 A. A user may input gesture  8 A into computing device  2 A via presence sensitive screen  4 A. Computing device  2 A, or server  12  in some examples, may identify gesture  8 A and present a list of shortcuts associated with identified gesture  8 A to the user of computing device  2 A, but the correct shortcut may not be displayed in the list of shortcuts  86 . A user may activate text entry field  20 , via tapping or other method of indicating selection, and begin entering text into text entry field  20 . 
       FIG. 10B  shows that computing device  2 A may hide the drawing area and display inputted gesture  8 A in a miniaturized version on the upper right  84 . Computing device  2 A may also display an expanded list of potential shortcuts  88  for selection by the user. The user may use an alternative input device, such as virtual keyboard  70 , to input the desired shortcut, allowing computing device  2 A to winnow the list of prospective shortcuts. 
       FIG. 10C  shows partially entered search string in text entry field  20  of computing device  2 A. Computing device  2 A, independently or in conjunction with server  12 , may search for shortcuts corresponding to the search string. The search may be performed dynamically, matching partially entered search strings as the search strings are entered into text entry field  20 . One or more shortcuts, such as shortcut  92 , may be presented to the user via output device  36  (e.g., presence sensitive screen  4 A) for user selection. 
       FIG. 10D  shows the selection of the desired shortcut. Selection of shortcut  92  causes computing device  2 A to perform the action associated with the selected shortcut and may cause computing device  2 A to learn the association between the inputted gesture  8 A and the selected shortcut for future queries, storing the customized gesture-shortcut association in local memory (e.g., memory  28  of computing device  2 A) for later recall. In some examples, computing device  2 A may transmit the customized gesture-shortcut association to server  12  for backup and to be used in the continued development of the gesture-based language. 
       FIGS. 11A-11D  are diagrams illustrating shortcut editing in an example gesture-based application installed on computing device  2 A shown in  FIG. 1 , in accordance with one or more aspects of the present disclosure. A user may customize or define a new gesture to represent a shortcut. 
       FIG. 11A  shows computing device  2 A equipped with a gesture-based application. A user may search for a desired gesture-shortcut association by entering text, e.g., via virtual keyboard  70 , in text entry field  20 . Computing device  2 A may display a list of potential shortcuts  86  matching the text query. Selection of shortcut  94 , by tapping or otherwise indicating the selection on presence sensitive screen  4 A of computing device  2 A, may cause computing device  2 A to open a new window in the gesture-based application allowing a user to edit the gesture or gesture-shortcut association represented by shortcut  94 . 
       FIG. 11B  shows a gesture-based application configured to allow a user to edit a gesture. Computing device  2 A may display one or more menus showing relevant data about the selected gesture-shortcut association, including the title of the shortcut  96  and URL the shortcut directs to  98 . For example, computing device may display a variety of gestures, such as gestures  102 , associated with the shortcut. One gesture (e.g., gesture  106 ) may be displayed in a darker color or otherwise set apart from the other gestures associated with the shortcut, indicating this gesture is most frequently associated with the shortcut by the user population. A user may elect to create a custom gesture for the shortcut by, for example, selecting Draw Your Own” button  100 . 
       FIG. 11C  shows a menu in a gesture-based application configured to allow a user to create a custom gesture for a particular shortcut. The user may input a gesture (e.g., gesture  106 ) that the user wishes to associate with the shortcut. Gesture  106  may be inputted by the user with a fingertip, stylus, or other tool on presence sensitive screen  4 A. Upon entry of gesture  106 , computing device  2 A may store gesture  106  locally in computing device  2 A (e.g., in memory  28 ). 
       FIG. 11D  shows a gesture-based application of computing device  2 A displaying a set of gestures associated with a particular shortcut, including gesture  106  (discussed in  FIG. 11C ). Computing device  2 A may display the new gesture  106  among the other gestures associated with the shortcut (e.g., by displaying customized gesture  108 ). Computing device  2 A may also transmit customized gesture  108 , or data representative of customized gesture  108 , to server  12  for backup and to continue development of the gesture-based language. 
     The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The term “processor” or “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit including hardware may also perform one or more of the techniques of this disclosure. 
     Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various techniques described in this disclosure. In addition, any of the described units, modules or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware, firmware, or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware, firmware, or software components, or integrated within common or separate hardware, firmware, or software components. 
     The techniques described in this disclosure may also be embodied or encoded in an article of manufacture including a computer-readable storage medium encoded with instructions. Instructions embedded or encoded in an article of manufacture including a computer-readable storage medium encoded, may cause one or more programmable processors, or other processors, to implement one or more of the techniques described herein, such as when instructions included or encoded in the computer-readable storage medium are executed by the one or more processors. Computer readable storage media may include random access memory (RAM), read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), flash memory, a hard disk, a compact disc ROM (CD-ROM), a floppy disk, a cassette, magnetic media, optical media, or other computer readable media. In some examples, an article of manufacture may include one or more computer-readable storage media. 
     In some examples, a computer-readable storage medium may include a non-transitory medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in RAM or cache). 
     Various embodiments have been described. These and other embodiments are within the scope of the following claims.