Patent Publication Number: US-8996351-B2

Title: Cloud-based translation service for multi-function peripheral

Description:
FIELD OF THE INVENTION 
     The present invention relates to a technique for translating, in a computing cloud, a document that was scanned by a multi-function peripheral. 
     BACKGROUND 
     Multi-function peripherals (MFPs), such as those manufactured and sold by Ricoh Corporation, are capable of performing a variety of different functions relative to paper documents. Such MFPs typically are capable of scanning documents, printing documents, copying documents, stapling documents, punching holes in documents, storing digital copies of scanned documents, etc. 
     Some MFPs even have the built-in capability to perform automatic processing of the digital copies of the documents that they scan. U.S. Pat. No. 7,769,249 (issued Aug. 3, 2010), assigned to Ricoh Company, Ltd., describes a document optical character recognition (OCR)-implementing device. The device includes a reading part that is configured to read a document and form a recognition image. The device additionally includes an obtaining part that is configured to perform image processing of the recognition image. The device additionally includes OCR engines that are configured to perform a character recognition process on the recognition image. Unfortunately, automatically performed OCR processing is prone to errors, and the results are often unsatisfactory to users. 
     The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. 
     SUMMARY 
     Techniques are provided for translating a document that was scanned by a multi-function peripheral (MFP). A server within a computing cloud receives (a) an MFP identifier of an MFP and (b) processed scan data that resulted from optical character recognition and/or natural language translation having been performed on scan data originally produced by the MFP. In response to the receipt of the processed scan data at the server, the server selects, from a group of context-specific rules sets, a particular context-specific rule set that is mapped to a context to which the MFP identifier is mapped; different context-specific rule sets may be mapped to different contexts, and different MFP identifiers may be mapped to different contexts. Corrected processed scan data is generated by applying the selected set of context-specific rules to the processed scan data that was received by the server. User-made manual corrections later made to the corrected processed scan data may be used to update the selected set of context-specific rules (while leaving other unselected sets of context-specific rules unaffected) so that those corrections are also made to other processed scan data produced by MFPs having identifiers mapped to the same context. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the drawings: 
         FIG. 1  is a block diagram that illustrates an example of a cloud-based translation service system, according to an embodiment of the invention; 
         FIG. 2  is a flowchart that illustrates an example of steps that might be taken by a user of the cloud-based translation system shown in  FIG. 1 , according to an embodiment of the invention; 
         FIGS. 3-5  are flowcharts that illustrate an example of a technique that may be performed collaboratively by various components of the cloud-based translation system shown in  FIG. 1 , according to an embodiment of the invention; 
         FIG. 6  is a flow diagram that illustrates a technique for correcting OCR output based on MFP context-specific rules, according to an embodiment of the invention; 
         FIG. 7  is a flow diagram that illustrates a technique for updating MFP context-specific OCR correction rules based on corrections that a user makes to the results of OCR processing, according to an embodiment of the invention; 
         FIG. 8  is a flow diagram that illustrates a technique for correcting natural language translation output based on MFP context-specific rules, according to an embodiment of the invention; 
         FIG. 9  is a flow diagram that illustrates a technique for updating translation correction rules based on corrections that a user makes to the results of natural language translation processing, according to an embodiment of the invention; 
         FIG. 10  is a diagram that illustrates an example of a user interface through which a user can view a translation from one natural language to another, according to an embodiment of the invention; 
         FIG. 11  shows an example of user-corrected translated text that corrects an error in machine-made translation shown in  FIG. 10 , according to an embodiment of the invention; 
         FIG. 12  is a diagram that illustrates an example database schema for a database that stores tables containing information about MFP contexts, documents, OCR correction rules, and translation correction rules, according to an embodiment of the invention; and 
         FIG. 13  is a block diagram that illustrates a computer system upon which an embodiment of the invention may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention. 
     General Overview 
     Using techniques described herein, a multi-function peripheral (MFP) scans a document and transmits the resulting scanned data to a server that resides within a computing cloud. The server (potentially acting in concert with other specialized servers also located in the computing cloud) performs services relative to the scanned data on behalf of the MFP. For example, such services may include optical character recognition and natural language translation relative to the written content contained in the scanned data. The MFP is associated with a unique device identifier that is only associated with that MFP and no other device that utilizes the cloud-based services. Using the MFP&#39;s device identifier, the cloud-based services can specially tailor the processing that is performed relative to data that is received from the MFP. Using the MFP&#39;s device identifier, the cloud-based services can classify (e.g., by purpose) scan data received from the MFP. The classification adds context that can be used to enhance services performed for the MFP (and for other MFPs having similar contexts); the enhancement may involve a more accurate optical character recognition and a more accurate natural language translation. Consequently, optical character recognition accuracy and natural language translation accuracy increases for all MFPs having similar purposes and providing similar types of documents. 
     After corrections have been automatically made to OCR processing results and natural language translation processing results using MFP context-specific rules that are stored in a database, users are given the opportunity to further correct the OCR processing results and natural language translation processing results. In one embodiment, these manually made corrections are used to update the MFP context-specific rules in the database so that automatic corrections made to future documents using those rules will be even more accurate. 
     Cloud-Based Translation Service System 
       FIG. 1  is a block diagram that illustrates an example of a cloud-based translation service system, according to an embodiment of the invention. The system illustrated in  FIG. 1  includes a multi-function peripheral (MFP)  102 . An Internet browser application, such as Microsoft Internet Explorer or Mozilla Firefox, executes on MFP  102 . The browser application presents, to a user of MFP  102 , graphical user interfaces through which the user can provide input to and receive information from MFP  102 . Through these interfaces, the user can request services from MFP  102 . One example of the services that the user can request from MFP  102  is a scanning service that MFP  102  provides; MFP  102  is, among other things, a scanning device. Services such as the scanning service are, in one embodiment, available via the browser through a local common gateway interface (CGI) and Javascript extensions that are included within the interface pages that the MFP  102  presents to the user through the browser application. A user using the scanning service of MFP  102  places a document within an input tray of MFP  102 . In response to the user&#39;s instruction to begin scanning, MFP  102  obtains each page of the document, one at a time, from the input tray, scans that image electronically, and produces and stores (in memory) scan data that represents a digital copy of that image. For example, the digital copy may be a bitmap, JPEG, GIF, or PDF image, to name a few formats. The document may contain writing in one of a variety of natural languages that are understandable to human beings. Such writing typically is composed of sequences of words, which are, in turn, made up of one or more characters each. Characters and the character sets to which they belong may differ from language to language. 
     Through the graphical user interface provided via the browser application, MFP  102  permits a user to instruct MFP  102  to transmit such a digital copy to a Software As A Service (SAAS) server  104 . SAAS server  104  does not reside in the same location as MFP  102 . SAAS server  104  is considered to be in a computing cloud  100 , and provides services to numerous different devices all over the world. SAAS server  104  typically is not owned, operated, or maintained by the owners, operators, or users of MFP  102 . MFP  102  is only one of the devices to which SAAS server  104  provides services. MFP  102  interacts with SAAS server  104  over a series of networks which may include local area networks (LANs) and, typically, the Internet. Thus, MFP  102  sends data to SAAS server  104  using the Hypertext Transfer Protocol (HTTP) and/or the secure version of that protocol (HTTPS). 
     Along with the digital copy of the scanned document that MFP  102  transmits to SAAS server  104 , MFP  102  additionally transmits device identification data to SAAS server  104 . The device identification data uniquely identifies MFP  102  such that no other device in the world that communicates with SAAS server  104  possesses the same device identifier. MFP  102  may locally store its device identifier, which may be configured by users of MFP  102 , so that MFP  102  can transmit its device identifier to SAAS server  104  whenever MFP  102  requests services from SAAS server  104 . In addition to the scanned document data and the device identification data, MFP  102  may send, to SAAS server  104 , instructions indicating the kinds of services that MFP  102  wants from cloud  100 . Such services may be selected by a user of MFP  102  using the graphical user interface that MFP  102  displays via the browser interface. Such services may include, for example, optical character recognition services and natural language translation services. For example, a user of MFP  102  may indicate that he wants the contents of the scanned document to be translated from a source natural language into a target natural language that differs from the source natural language. 
     SAAS server  104  interacts with other servers in computing cloud  100 . In one embodiment, SAAS server  104  interacts with these other servers using application programming interfaces (APIs) that those other servers expose to external entities. SAAS server  104  may utilize the services offered by these other servers, for example, by invoking methods of those APIs with the appropriate parameters. In one embodiment, the other servers whose services SAAS server  104  uses include an optical character recognition (OCR) server  106  and a natural language translation server  108 . Using an OCR API exposed by OCR server  106 , SAAS server  104  may instruct OCR server  106  to perform OCR services relative to the scanned image data that SAAS server  104  received from MFP  102 . The result of the performance of OCR upon the scanned image data is output that specifies sequences of words, comprised of characters, in a natural language. SAAS server  104  also may instruct a translation server  108 , also in computing cloud  100 , to perform natural language translation on the output produced by OCR server  106 . SAAS server  104  may use a translation API exposed by translation server  108  to tell translation server  108  the source and target natural languages for translation. Along with other data that SAAS server  104  passes to OCR server  106  and translation server  108 , SAAS server  104  may pass the unique device identifier that SAAS server  104  received from MFP  102  in connection with the scanned image data. 
     In one embodiment, SAAS server  104  acts as an intermediate broker for all of the other servers in computing cloud  100 . For example, SAAS server  104  may send scanned image data to OCR server  106 , receive processed data (i.e., optically-recognized character strings) from OCR server  106 , send that processed data to translation server  108 , and receive further processed data (i.e., the character strings translated into a different natural language) from translation server  108 . In an alternative embodiment, however, SAAS server  104  instead instructs each server regarding the next step in a chain of services. In such an embodiment, OCR server  106  may react to these instructions by transmitting its processed output data directly to translation server  108  without further interaction with SAAS server  104 . Such instructions, in one embodiment, also include instructions regarding the final processing of the processed data after it has been through the entire server chain. Such instructions may indicate that the processed data is to be e-mailed to a specified e-mail address, and/or printed by a specified printer with a specified Internet address, and/or saved within a specified directory on a specified storage server at a specified Internet address. In one embodiment, these instructions are initially specified by the user of MFP  102  through the graphical user interface, and are passed along from server to server in computing cloud  100  along with the scan data and the unique identifier of MFP  102 . The final server in the chain may carry out the disposition instructions for the finally processed data (e.g., data that has undergone both OCR and natural language translation). 
     Setting Up the MFP 
     In one embodiment, an administrator of MFP  102  initially sets up MFP  102  with configuration information that is then stored locally in the memory of MFP  102 . MFP  102  contains a database that stores configuration information. Among other information that the administrator inputs into this database while configuring MFP  102  is context data that identifies the purpose for which the MFP  102  is used. Such context data may identify the department that primarily uses MFP  102 , for example. Under these circumstances, the context data might indicate that MFP  102  is used by an engineering department or a marketing department. As will be seen from the discussion below, the services that are provided to MFP  102  by the servers in computing cloud  100  may be narrowed, refined, and specialized over time based on the context that is represented within the context data with which MFP  102  is initially configured. Additionally or alternatively, the context data may specify a device type of MFP  102 . 
     Additionally, in one embodiment, the administrator of MFP  102  registers MFP  102  with SAAS server  104  in computing cloud  100 . Such registration typically includes the assignment of the unique device identifier to MFP  102 , which MFP  102  then sends, at registration time, to SAAS server  104  for storage. The registration also includes the transmission of the assigned context data to SAAS server  104  for storage. SAAS server  104  stores a mapping in between the unique device identifier of MFP  102  and the context data that indicates the context or purpose of MFP  102 . The mapping of the device identifier to the context data defines which set of rules will be associated with MFP  102 . Such rules may be used by various servers in computing cloud  100  to increase the accuracy of the services that they provide to MFP  102 . This is because when such a cloud server knows the context of MFP  102 , it is able to apply rules (e.g., OCR rules and/or natural language translation rules) that are appropriate specifically for that context and possibly to no other context. 
     Example User Workflow 
       FIG. 2  is a flowchart that illustrates an example of steps that might be taken by a user of the cloud-based translation system shown in  FIG. 1 . In block  202 , the user scans a document using the MFP (e.g., MFP  102 ). In block  204 , after OCR services have been performed (e.g., by OCR server  106 ) on the resulting scan data, the user uses a browser application on his client personal computer in order to make corrections, if needed, to the output of the OCR process. In block  206 , after natural language translation services have been performed (e.g., by translation server  108 ) on the corrected OCR output, the user uses the browser application on his client personal computer in order to make corrections, if needed, to the output of the natural language translation process. In block  208 , the user is given the option to print and/or e-mail the corrected translated document. 
     Example System Flow 
       FIGS. 3-5  are flowcharts that illustrate an example of a technique that may be performed collaboratively by various components of the cloud-based translation system shown in  FIG. 1 . Referring first to  FIG. 3 , in block  302 , the MFP (e.g., MFP  102 ) scans a document using a scanning application available on the MFP. The MFP stores document details in a document table within a database stored on the MFP. The MFP also assigns a document identifier to the document. 
     In block  304 , the MFP pre-processes the scanned image to improve contrast, correct skew, etc. In block  306 , the MFP then feeds the image into a standard OCR engine within computing cloud  100  (e.g., OCR server  106 ), potentially via SAAS server  104 . In block  308 , Process A, discussed in greater detail below in connection with  FIG. 6 , is performed. Based on the MFP device identifier, specific rules may be applied to update the results of the OCR processing. Control then passes to block  402  of  FIG. 4 . 
     Referring now to  FIG. 4 , in block  402 , a user interface is provided to the user so that the user can review both the original document and the results of the OCR processing. The user is given the option to verify and correct the OCR results online. If the user chooses to exercise this option, control passes to block  404 . If the user chooses not to exercise this option, then control passes to block  406 . Any changes that the user does make are fed back into the database on the MFP. These changes are saved in association with the MFP&#39;s context data. The document is saved so that translation can begin. 
     In block  404 , Process B, discussed in greater detail below in connection with  FIG. 7 , is performed. Control then passes to block  406 . 
     In block  406 , the processed data resulting from the OCR processing and any post-processing that has been performed on those OCR results are fed into a translation service (e.g., translation server  108 ) within computing cloud  100 . In block  408 , Process C, discussed in greater detail below in connection with  FIG. 8 , is performed. The results of the translation service are post-processed based on MFP-specific rules. Control then passes to block  502  of  FIG. 5 . 
     Referring now to  FIG. 5 , in block  502 , a user interface is provided to the user so that the user can review both the original document and the results of the natural language translation processing. The user is given the option to verify and correct the translation results online. If the user chooses to exercise this option, then control passes to block  504 . If the user chooses not to exercise this option, then control passes to block  506 . Any changes that the user does make are fed back into the database on the MFP. These changes are saved in association with the MFP&#39;s context data. The document is saved. 
     In block  504 , Process D, discussed in greater detail below in connection with  FIG. 9 , is performed. Control then passes to block  506 . 
     In block  506 , the user is given the option to print and/or e-mail the translated document, or to save the translated document for later printing and/or e-mailing. In block  508 , the technique illustrated in  FIGS. 3-5  ends. 
     Process A—Context-Specific Automatic OCR Correction 
       FIG. 6  is a flow diagram that illustrates a technique for correcting OCR output based on MFP context-specific rules, according to an embodiment of the invention. The steps illustrated in  FIG. 6  collectively form “Process A” referenced above in  FIGS. 3-5 . The results of OCR processing performed on the scanned document are referred to below as the OCR document. The OCR document contains text that was extracted from the image produced by the MFP&#39;s scanner. OCR engine processing may not be absolutely accurate, so some errors may be present within the OCR document. By applying, to the OCR document, MFP context-specific rules that are stored in the database and generated using techniques described herein, the accuracy of OCR processing results can be improved. Software (e.g., software executing on SAAS server  104 ) generates a list of rules that are associated with the MFP&#39;s device identifier. These rules are then applied to the OCR document in order to correct errors automatically. 
     In block  602 , the OCR document and the MFP&#39;s device identifier are obtained. The OCR document may be obtained from OCR server  106 , for example. Document details for the document can be found in a “Document” table in a database. Context-specific details for the MFP can be found using the MFP&#39;s device identifier in a “Device Type” table in a database. 
     In block  604 , OCR rules for the MFP are obtained from a database. The rules applicable to the MFP may be found in an “OCR Rules” table in a database. The specific rules to be applied are associated in the table with the MFP&#39;s context data. 
     In block  606 , a list of text patterns to search for in the OCR document is created. The list of patterns is constructed based on the “OCR Rules” that are associated with the MFP&#39;s context data. For example, if the MFP&#39;s context data indicates an “engineering” context, then all of the rules in the “OCR Rules” table that are associated with the “engineering” context and the OCR document&#39;s language are selected for application to the OCR document. Generally, each rule specifies a set of characters that are to be replaced (the text patterns) and a set of characters to replace the characters that are to be replaced. 
     In block  608 , text in the OCR document that matches text to be replaced, as indicated in the list of text patterns created in block  606 , is automatically replaced with the corresponding replacement text that is mapped, in the rules, to the matching text. In one embodiment, replacement rules are applied based on priority or importance. Some rules may be ranked higher than others. Higher-ranked rules are applied before lower-ranked rules are. Each rule in the “OCR Rules” table may be associated with a “Usage Count” field. The value stored in this field may be used to determine a rule&#39;s rank. In one embodiment, if two or more rules conflict, then “Left Text” and “Right Text” fields in the “OCR Rules” table are used to select one of the conflicting rules whose application is most suitable. 
     Process B—Updating OCR Correction Rules 
       FIG. 7  is a flow diagram that illustrates a technique for updating OCR correction rules based on corrections that a user makes to the results of OCR processing, according to an embodiment of the invention. The steps illustrated in  FIG. 7  collectively form “Process B” referenced above in  FIGS. 3-5 . A user interface is provided to the user. The user interface allows the user to view the original scanned document image and the OCR document (potentially with some automatic rules-based corrections already made) side-by-side. The user can use the user interface to make additional corrections. The input from the user is stored in a database and is used by Process A to make automatic rules-based OCR corrections in the future. Thus, the OCR replacement rules may be automatically updated based on the corrections that the user specifies. 
     In block  702 , the document identifier and the MFP&#39;s device identifier are obtained. Document details for the document can be found in a “Document” table in a database. Context-specific details for the MFP can be found using the MFP&#39;s device identifier in a “Device Type” table in a database. In block  704 , the original scanned document and the OCR document are shown to the user side-by-side. For example, these documents may be shown to the user side-by-side in a browser application executing on the user&#39;s client personal computer. In block  706 , the user uses the user interface (e.g., provided via the browser application) to perform manual corrections to the OCR document. In block  708 , the user is given the option to save or abandon the corrections made. If the user opts to save the corrections, then control passes to block  712 . If the user opts to abandon the corrections, then control passes to block  710 . 
     In block  710 , the side-by-side view of the documents is exited without saving the corrections and without updating the OCR replacement rules. Alternatively, in block  712 , the corrections made are stored in the database. The corrections are stored in the “OCR Rules” table. The corrections are associated in the table with the MFP&#39;s context data. As is discussed above in connection with  FIG. 6 , these are the rules that are applied in “Process A” to improve the accuracy of optical character recognition. 
     Process C—Context-Specific Automatic Translation Correction 
       FIG. 8  is a flow diagram that illustrates a technique for correcting natural language translation output based on MFP context-specific rules, according to an embodiment of the invention. The steps illustrated in  FIG. 8  collectively form “Process C” referenced above in  FIGS. 3-5 . The results of natural language translation processing performed on the OCR document are referred to below as the translated document. The translated document contains text that was translated from the source natural language of the OCR document (and the original scanned document) to a different, target natural language. Natural language translation engine processing may not be absolutely accurate, so some errors may be present within the translated document. By applying, to the translated document, MFP context-specific rules that are stored in the database and generated using techniques described herein, the accuracy of natural language translation processing results can be improved. Software (e.g., software executing on SAAS server  104 ) generates a list of rules that are associated with the MFP&#39;s device identifier. These rules are then applied to the translated document in order to correct errors automatically. 
     In block  802 , the translated document and the MFP&#39;s device identifier are obtained. The translated document may be obtained from translation server  108 , for example. Document details for the document can be found in a “Document” table in a database. Context-specific details for the MFP can be found using the MFP&#39;s device identifier in a “Device Type” table in a database. 
     In block  804 , translation rules for the MFP are obtained from a database. Other details about the MFP may be obtained from the database as well. The rules applicable to the MFP may be found in a “Translation Rules” table in a database. The specific rules to be applied are associated in the table with the MFP&#39;s context data. 
     In block  806 , a list of text patterns to search for in the translated document is created. The list of patterns is constructed based on the “Translation Rules” that are associated with the MFP&#39;s context data. For example, if the MFP&#39;s context data indicates an “engineering” context, then all of the rules in the “Translation Rules” table that are associated with the “engineering” context and the translated document&#39;s language are selected for application to the translated document. Generally, each rule specifies a set of characters that are to be replaced (the text patterns) and a set of characters to replace the characters that are to be replaced. 
     In block  808 , text in the translated document that matches text to be replaced, as indicated in the list of text patterns created in block  806 , is automatically replaced with the corresponding replacement text that is mapped, in the rules, to the matching text. In one embodiment, replacement rules are applied based on priority or importance. Some rules may be ranked higher than others. Higher-ranked rules are applied before lower-ranked rules are. Each rule in the “Translation Rules” table may be associated with a “Usage Count” field. The value stored in this field may be used to determine a rule&#39;s rank. In one embodiment, if two or more rules conflict, then “Left Text” and “Right Text” fields in the “Translation Rules” table are used to select one of the conflicting rules whose application is most suitable. 
     Process D—Updating Translation Correction Rules 
       FIG. 9  is a flow diagram that illustrates a technique for updating translation correction rules based on corrections that a user makes to the results of natural language translation processing, according to an embodiment of the invention. The steps illustrated in  FIG. 9  collectively form “Process D” referenced above in  FIGS. 3-5 . A user interface is provided to the user. The user interface allows the user to view the original scanned document image and the translated document (potentially with some automatic rules-based corrections already made) side-by-side. The user can use the user interface to make additional corrections. The input from the user is stored in a database and is used by Process C to make automatic rules-based translation corrections in the future. Thus, the translation replacement rules may be automatically updated based on the corrections that the user specifies. 
     In block  902 , the document identifier and the MFP&#39;s device identifier are obtained. Document details for the document can be found in a “Document” table in a database. Context-specific details for the MFP can be found using the MFP&#39;s device identifier in a “Device Type” table in a database. In block  904 , the original scanned document and the translated document are shown to the user side-by-side. For example, these documents may be shown to the user side-by-side in a browser application executing on the user&#39;s client personal computer. In block  906 , the user uses the user interface (e.g., provided via the browser application) to perform manual corrections to the translated document. In block  908 , the user is given the option to save or abandon the corrections made. If the user opts to save the corrections, then control passes to block  912 . If the user opts to abandon the corrections, then control passes to block  910 . 
     In block  910 , the side-by-side view of the documents is exited without saving the corrections and without updating the translation replacement rules. Alternatively, in block  912 , the corrections made are stored in the database. The corrections are stored in the “Translation Rules” table. The corrections are associated in the table with the MFP&#39;s context data. As is discussed above in connection with  FIG. 8 , these are the rules that are applied in “Process C” to improve the accuracy of natural language translation. 
       FIG. 10  is a diagram that illustrates an example of a user interface through which a user can view a translation from one natural language to another. A field  1002  contains text that is in a source natural language (in this example, English). The source text reads “I am an example of translation.” Result  1004  shows a translation of the text into a target natural language (in this example, Hindi). The translation shown is machine-made and automatic. However, the translation is not entirely correct. As is discussed above, embodiments of the invention allow the user to make corrections to machine-made natural language translation results. 
       FIG. 11  shows an example of user-corrected translated text that corrects an error in machine-made translation shown in  FIG. 10 . In one embodiment, as discussed above, user-made corrections to machine-made translations are used to update translation correction rules in the “Translation Rules” table in the database. In the case of this specific example, the next time that a document is translated from English to Hindi, the translation correction software will look for the pattern “I am an example of translation” and replace the machine-made translation text with the user-corrected translation text shown in  FIG. 11 . 
     Example Database Schema 
     As is discussed above, in one embodiment, a database (potentially stored within SAAS server  104 ) contains multiple tables. These tables contain MFP context-specific information, context-specific OCR correction rules, and context-specific translation correction rules, for example. Discussed below are the structures and formats of tables that are stored in the database according to one embodiment. 
     The “DeviceType” table stores the known MFP contexts and identifiers associated with those contexts. As is discussed above, an administrator specifies, for each MFP, a context classification that indicates that MFP&#39;s device or purpose. Such context classifications may include “engineering,” “marketing,” or “legal,” for example, if these are the departments that primarily use the corresponding MFPs. Because each MFP is associated with a context, and because correction rules are context-specific, the correction rules that are applied to documents scanned by one MFP may differ from correction rules that are applied to documents scanned by another MFP. For each MFP, the correction rules that are applied to documents scanned by that MFP are specifically tailored for correcting the kinds of words that are likely to be found in the vocabulary or jargon that is peculiar to that MFP&#39;s context. Because users having the MFP&#39;s same context make corrections that are used to update the context-specific rules, the rules for each context are likely to become even more context-specific and produce better OCR and natural language translation corrections for those contexts. 
     Table 1 shows an example of the structure of the “DeviceType” table. The device type classification field stores an MFP context and the Device Type ID field stores a context identifier for that context. Thus, the “DeviceType” table stores the universe of existing contexts that may be assigned to MFPs. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 DEVICETYPE 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Device Type ID (long) 
                 Device Type Classification (varchar 1000) 
               
               
                   
                   
               
            
           
         
       
     
     The “MFPDeviceInfo” table stores the context for each MFP. Users register each MFP in part by selecting, from the existing universe of recognized contexts, a specific context (or device type) for that MFP. If the existing universe of recognized contexts (contained in the “DeviceType” table) does not include the desired context, then users can ask an administrator to create a new context in the “DeviceType” table. Table 2 shows an example of the structure of the “MFPDeviceInfo” table. In one embodiment, it is possible for the same MFP to be associated with multiple different contexts in the “MFPDeviceInfo” table. This may be the case when multiple different departments share the same MFP. The “MFP Device ID” field stores the unique device identifier for the MFP. The “Device Type ID” field contains the context identifier that identifies the context for that MFP. The context matching the context identifier can be looked up using the context identifier in the “DeviceType” table discussed above. 
     
       
         
           
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 MFPDEVICEINFO 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Device Type ID (long) 
                 MFP Device ID (varchar 1000) 
               
               
                   
                   
               
            
           
         
       
     
     The “Document” table contains a separate entry for each document that is scanned by an MFP. Thus, in one embodiment, whenever an MFP scans a document, a new entry is created in this table. When a particular document has OCR processing or natural language translation processing performed upon it, the entry for the particular document is automatically updated to indicate the location of the corresponding OCR document and/or translation document. Table 3 shows an example of the structure of the “Document” table. The “Device Type ID” field indicates the context identifier for the context of the MFP that scanned the original document. 
     
       
         
           
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 DOCUMENT 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Document 
                 Device 
                 Original 
                 Translated 
                 Original 
                 OCR 
                 Translated 
                 Subject 
                 Misc Info 
               
               
                 ID (long) 
                 Type ID 
                 Document 
                 Document 
                 Document 
                 Document 
                 Document 
                 Field 
                 (varchar 
               
               
                   
                 (long) 
                 Language 
                 Language 
                 Location 
                 Location 
                 Location 
                 (varchar 
                 1000) 
               
               
                   
                   
                 (varchar 
                 (varchar 
                 (varchar 
                 (varchar 
                 (varchar 
                 1000) 
               
               
                   
                   
                 1000) 
                 1000) 
                 1000) 
                 1000) 
                 1000) 
               
               
                   
               
            
           
         
       
     
     The “OCR Rules” table contains the text replacement rules that are applicable to OCR documents. In one embodiment, each rule specifies at least a set of characters to be replaced and a corresponding set of replacement characters to replace the former set. The rules are used by “Process A,” discussed above, to correct OCR document errors automatically. Corrections that users manually make in “Process B,” discussed above, are used to generate new entries in the “OCR Rules” table. Table 4 shows an example of the structure of the “OCR Rules” table. The “Usage Count” field is updated each time that a rule is applied to an OCR document. As is discussed above, a rule&#39;s usage count may be used to determine the priority of that rule&#39;s application relative to other rules. Rules with higher usage counts may be applied before rules with lower usage counts. In one embodiment, only rules having the same specified language as the OCR document&#39;s original language are applied to that OCR document. In one embodiment, only rules having the same “Device Type ID” (which indicates MFP context) as the OCR document are applied to the OCR document. 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 OCRRULES 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Language 
                 Device 
                 Original 
                 Left Text 
                 Right 
                 Replacement 
                 Usage 
                 Subject 
                 Misc Info 
               
               
                 (varchar 
                 Type ID 
                 Text 
                 (varchar 
                 Text 
                 Text 
                 Count 
                 Field 
                 (varchar 
               
               
                 1000) 
                 (long) 
                 (varchar 
                 1000) 
                 (varchar 
                 (varchar 
                 (long) 
                 (varchar 
                 1000) 
               
               
                   
                   
                 1000) 
                   
                 1000) 
                 1000) 
                   
                 1000) 
               
               
                   
               
            
           
         
       
     
     The “Translation Rules” table contains the text replacement rules that are applicable to translated documents. In one embodiment, each rule specifies at least a set of characters to be replaced and a corresponding set of replacement characters to replace the former set. The rules are used by “Process C,” discussed above, to correct translated document errors automatically. Corrections that users manually make in “Process D,” discussed above, are used to generate new entries in the “Translation Rules” table. Table 5 shows an example of the structure of the “Translation Rules” table. The “Usage Count” field is updated each time that a rule is applied to a translated document. As is discussed above, a rule&#39;s usage count may be used to determine the priority of that rule&#39;s application relative to other rules. Rules with higher usage counts may be applied before rules with lower usage counts. In one embodiment, only rules having either the same specified original language as the document&#39;s original language or the same specified translated language as the document&#39;s translated language are applied to that translated document. In one embodiment, only rules having the same “Device Type ID” (which indicates MFP context) as the translated document are applied to the translated document. 
     
       
         
           
               
             
               
                 TABLE 5 
               
               
                   
               
               
                 TRANSLATIONRULES 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
            
               
                 Original 
                 Translated 
                 Device 
                 Original 
                 Left 
                 Right 
                 Replacement 
                 Usage 
                 Subject 
                 Misc 
               
               
                 Document 
                 Document 
                 Type 
                 Text 
                 Text 
                 Text 
                 Text 
                 Count 
                 Field 
                 Info 
               
               
                 Language 
                 Language 
                 ID 
                 (varchar 
                 (varchar 
                 (varchar 
                 (varchar 
                 (long) 
                 (varchar 
                 (varchar 
               
               
                 (varchar 
                 (varchar 
                 (long) 
                 1000) 
                 1000) 
                 1000) 
                 1000) 
                   
                 1000) 
                 1000) 
               
               
                 1000) 
                 1000) 
               
               
                   
               
            
           
         
       
     
       FIG. 12  is a diagram that illustrates an example database schema for a database that stores tables containing information about MFP contexts, documents, OCR correction rules, and translation correction rules, according to an embodiment of the invention. The database schema shown includes each of the tables discussed above, including “OCR Rules” table  1202 , “Translation Rules” table  1204 , “DeviceType” table  1206 , “MFPDeviceType” table  1208 , and “Document” table  1210 . 
     Hardware Overview 
     According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques. 
     For example,  FIG. 13  is a block diagram that illustrates a computer system  1300  upon which an embodiment of the invention may be implemented. Computer system  1300  includes a bus  1302  or other communication mechanism for communicating information, and a hardware processor  1304  coupled with bus  1302  for processing information. Hardware processor  1304  may be, for example, a general purpose microprocessor. 
     Computer system  1300  also includes a main memory  1306 , such as a random access memory (RAM) or other dynamic storage device, coupled to bus  1302  for storing information and instructions to be executed by processor  1304 . Main memory  1306  also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor  1304 . Such instructions, when stored in non-transitory storage media accessible to processor  1304 , render computer system  1300  into a special-purpose machine that is customized to perform the operations specified in the instructions. 
     Computer system  1300  further includes a read only memory (ROM)  1308  or other static storage device coupled to bus  1302  for storing static information and instructions for processor  1304 . A storage device  1310 , such as a magnetic disk or optical disk, is provided and coupled to bus  1302  for storing information and instructions. 
     Computer system  1300  may be coupled via bus  1302  to a display  1312 , such as a cathode ray tube (CRT), for displaying information to a computer user. An input device  1314 , including alphanumeric and other keys, is coupled to bus  1302  for communicating information and command selections to processor  1304 . Another type of user input device is cursor control  1316 , such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor  1304  and for controlling cursor movement on display  1312 . This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. 
     Computer system  1300  may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system  1300  to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system  1300  in response to processor  1304  executing one or more sequences of one or more instructions contained in main memory  1306 . Such instructions may be read into main memory  1306  from another storage medium, such as storage device  1310 . Execution of the sequences of instructions contained in main memory  1306  causes processor  1304  to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. 
     The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operation in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device  1310 . Volatile media includes dynamic memory, such as main memory  1306 . Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge. 
     Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus  1302 . Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications. 
     Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor  1304  for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system  1300  can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus  1302 . Bus  1302  carries the data to main memory  1306 , from which processor  1304  retrieves and executes the instructions. The instructions received by main memory  1306  may optionally be stored on storage device  1310  either before or after execution by processor  1304 . 
     Computer system  1300  also includes a communication interface  1318  coupled to bus  1302 . Communication interface  1318  provides a two-way data communication coupling to a network link  1320  that is connected to a local network  1322 . For example, communication interface  1318  may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface  1318  may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface  1318  sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. 
     Network link  1320  typically provides data communication through one or more networks to other data devices. For example, network link  1320  may provide a connection through local network  1322  to a host computer  1324  or to data equipment operated by an Internet Service Provider (ISP)  1326 . ISP  1326  in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet”  1328 . Local network  1322  and Internet  1328  both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link  1320  and through communication interface  1318 , which carry the digital data to and from computer system  1300 , are example forms of transmission media. 
     Computer system  1300  can send messages and receive data, including program code, through the network(s), network link  1320  and communication interface  1318 . In the Internet example, a server  1330  might transmit a requested code for an application program through Internet  1328 , ISP  1326 , local network  1322  and communication interface  1318 . 
     The received code may be executed by processor  1304  as it is received, and/or stored in storage device  1310 , or other non-volatile storage for later execution. 
     In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction.