Method and system for training classification and extraction engine in an imaging solution

A method and system for automatically training a document imaging classification and extraction system that switches between a manual mode and an automatic mode based on constant monitoring. A specialized sub-system monitors and records a user interaction with the classification system during the initial manual mode and, in parallel, develops and tests a user configuration with respect to an automated processing engine. The system is capable of being shifted to the automatic mode if a desired acceptability threshold is attained and the document can then be processed automatically. Furthermore, a user can interact with the classification system if the automatic mode fails. Information concerning exception handling can be entered into a training database for continual refinement of the classification and extraction system.

TECHNICAL FIELD

Embodiments are generally related to document management systems and methods. Embodiments also relate in general to the field of computers and similar technologies and in particular to software utilized in this field. Embodiments are additionally related to document imaging classification and extraction methods and systems.

BACKGROUND OF THE INVENTION

Document imaging is a process of scanning a paper document and converting the document to a digital image which is then stored via a magnetic storage device. Such document imaging processes provide the ability to perform an optical character recognition (OCR) for the translation of images of text such as scanned documents, into actual text characters. Classification is an important feature with respect to document image processing and is often a preliminary step towards recognition, understanding, and information extraction.

The majority of prior art techniques for classifying documents are both time consuming and labor intensive. Typically, the documents are processed manually and the classification of the document imaging requires training via a representative sample image to perform complex mathematical analysis, which cluster or classify documents that are similar to one another. Such techniques require significant training and technical resources. Furthermore, such approaches may not cover every classification/extraction scenario and are particularly limited by the representative samples provided.

Based on the foregoing, it is believed that a need exists for an improved method for automatically training a document imaging classification and extraction system. A need also exists for automatically switching between a manual mode and an automatic mode based on constant monitoring, as described in greater detailed herein.

BRIEF SUMMARY

It is, therefore, one aspect of the present invention to provide for an improved data-processing method, system, and computer-usable medium.

It is another aspect of the present invention to provide for an improved method for automatically training a document imaging classification and extraction system.

It is a further aspect of the present invention to provide for an improved method and system for automatically switching between a manual mode and an automatic mode based on constant monitoring.

It is yet another aspect of the present invention to provide for an improved method for the continual refinement of a document imaging classification and extraction system.

The aforementioned aspects and other objectives and advantages can now be achieved as described herein. A method and system for automatically training a document imaging classification and extraction system that switches between a manual mode and an automatic mode based on constant monitoring is disclosed. A specialized sub-system monitors and records a user interaction with the classification system during the initial manual mode and in parallel, develops and tests a user configuration for an automated processing engine. The system can be automatically shifted to the automatic mode, if a desired acceptability threshold is achieved, and the document can be processed automatically. Furthermore, the user can interact with the classification system if the automatic mode fails. Information with respect to such exception handling can be inputted into a training database for continual refinement of the automated classification and extraction system.

If the quality of incoming documents changes significantly, the sub-system can switch back to the manual mode by effectively rebooting the system until the acceptability threshold value is reached again. User actions, along with copies of the actual images, can be independently recorded in the training database, thereby allowing ground-truth data to be gathered over time while “real” work is being accomplished. The data generally includes information such as region-of-interest (x, y coordinates) based on a user's zoom and other actions related to a particular metadata field. Notifications indicative of health and performance of the system can be generated in the form of one or more reminders when the classification system begins operations. Such an approach provides for automatic switching between the manual and automated phases based on constant monitoring.

DETAILED DESCRIPTION

As depicted inFIG. 1, the present invention may be embodied in the context of a data-processing apparatus100comprising, for example, a central processor101, a main memory102, an input/output controller103, a keyboard104, a pointing device105(e.g., mouse, track ball, pen device, or the like), a display device106, and a mass storage107(e.g., hard disk). Additional input/output devices, such as a rendering device108, may be included in association with the data-processing apparatus100as desired. The rendering device108may be a standalone single function device such as a dedicated printer, scanner, copy machine, etc, or may be an apparatus such as an MFD (multifunction device). As illustrated, the various components of the data-processing apparatus100communicate through a system bus110or similar architecture.

FIG. 2illustrates a computer software system150for directing the operation of the data-processing apparatus100depicted inFIG. 1. Software system150, which is stored in system memory102and on disk memory107, can include a kernel or operating system151and a shell or interface153. A user325may interact with the computer software system150via such an interface153. One or more application programs, such as application software152, may be “loaded” (i.e., transferred from storage107into memory102) for execution by the data-processing apparatus100. The data-processing apparatus100receives user commands and data through user interface153; these inputs may then be acted upon by the data-processing apparatus100in accordance with instructions from operating module151and/or application module152.

The interface153, which is preferably a graphical user interface (GUI), also serves to display results, whereupon the user may supply additional inputs or terminate a given session. In one possible embodiment, operating system151and interface153can be implemented in the context of a “Windows” system. It can be appreciated, of course, that other types of systems are possible. For example, rather than a traditional “Windows” system, other operation systems such as, for example, Linux may also be employed with respect to operating system151and interface153. Module152can be adapted for automatically training a document imaging classification and extraction system. Module152can be adapted for automatically switching between a manual mode and an automatic mode based on constant monitoring. Application module152, on the other hand, can include instructions such as the various operations described herein with respect to the various components and modules described herein such as, for example, the method400depicted inFIG. 4.

The following description is presented with respect to embodiments of the present invention, which can be embodied in the context of a data-processing system such as data-processing apparatus100and computer software system150depicted respectivelyFIGS. 1-2. The present invention, however, is not limited to any particular application or any particular environment. Instead, those skilled in the art will find that the system and methods of the present invention may be advantageously applied to a variety of system and application software, including database management systems, word processors, and the like. Moreover, the present invention may be embodied on a variety of different platforms, including Macintosh, UNIX, LINUX, and the like. Therefore, the description of the exemplary embodiments, which follows, is for purposes of illustration and not considered a limitation.

FIG. 3illustrates a block diagram of a document imaging classification and extraction system300, in accordance with an embodiment. In general, document image classification can be an important step in office automation, digital libraries, and other document image analysis applications. The document image classification can be processed by training the document image classification and extraction system300manually on document images. The system300can switch between a manual mode and an automatic mode based on constant monitoring. The document image classification system300includes a document input section310, an independent sub-system330, a training database340and an automated processing engine350.

A document305that is to be classified can be received through the document input section310. The input section310inputs the document data and is capable of obtaining documents and groups of documents via the keyboard104, the rendering device108such as printers, scanners, photocopy machines, and the like comprising an OCR function. A document is a collection of one or more sentences written in a natural language, comprising letters, rows of letters, numbers, and the like, which are organized into a meaningful arrangement to form one document. Furthermore, a collection of multiple documents may be referred to as a document cluster.

A user325can manually classify the received document305through a user interface153by visual cross reference to an example set by following a manual indexing process in the manual mode. The user325can also extract metadata interactively by keying from the document image and/or utilizing “point and click” OCR (Optical Character Recognition) technology. Such OCR technology can be utilized for mechanical or electronic translation of images of handwritten, typewritten or printed text (e.g., usually captured by a scanner) into machine-editable text. The independent sub-system330monitors the actions of the user325with respect to the user interface153. The actual document image345can be stored in the training database340.

The user interface153, which is preferably a graphical user interface (GUI), can serve to display results, whereupon the user325may supply additional inputs or terminate a given session. Note that the term “GUI” generally refers to a type of environment that represents programs, files, options and so forth by means of graphically displayed icons, menus, and dialog boxes on a computer monitor screen. A user can interact with the GUI153to select and activate such options by pointing and clicking with a user input device such as, for example, a pointing device such as a mouse, and/or with a keyboard. A particular item can function in the same manner to the user in all applications because the GUI153provides standard software routines (e.g., module152) to handle these elements and reports the actions of the user325.

The actions of the user325maybe, for example, actions such as a zoom to a particular region of interest (e.g., x, y coordinates), and actions related to a particular metadata field of the document image and the like. The sub-system330in parallel develops and tests a user configuration with respect to the automated processing engine350. The sub-system330develops and tests the user classification and extraction models to determine if a minimum acceptability threshold has been attained. Note that as utilized herein, the term “metadata” generally refers to “data about data”, or of any sort of data contained in any media. An item of metadata may describe an individual datum, or content item, or a collection of data including multiple content items and hierarchical levels, for example, a database schema Metadata may include descriptive information concerning the context, quality and condition, or characteristics of the data.

The system300can be automatically shifted to an automatic mode to generate a classified document360by the automatic process engine350if a desired acceptability threshold is achieved. Furthermore, the user325can interact with the classification system300in order to handle exception cases if the automatic techniques fail. The information with respect to the exception handling can be entered into the training database340for continual refinement of the automated classification and extraction system300. The classified documents360are organized in a set of different classes, each class being representative of a given type of document, according to the definitions given manually.

Furthermore, if the quality or nature of the incoming documents changes significantly, the process can be switched back to the manual mode, effectively rebooting the system300until the acceptability threshold value is reached again. The training data can be gathered from the end-user documents and specific business process queue via transports such as fax, distributed scan from multi-function device, ftp (File Transfer Protocol), and web submission. Notifications indicative of the health and performance of the system300can be generated in the form of one or more reminders when the classification system300begins operations. Note that the system300can be a data processing system, such as data processing system100depicted inFIG. 1, and can include computer-implemented medium for automatically classifying the document305by switching between the manual mode and the automatic mode based on constant monitoring.

FIG. 4illustrates a flow chart of operations illustrating logical operational steps of a method400for automatically training the document imaging classification and extraction system300to switch between the manual mode and the automatic mode based on constant monitoring, in accordance with an embodiment. Note that inFIGS. 1-4, identical or similar blocks are generally indicated by identical reference numerals. The document305to be classified can be received via the document input section310, as illustrated at block410. Thereafter, a determination can be made whether the acceptability threshold value is met, as depicted at the block420.

If the acceptability threshold value is not met, the incoming document305can be manually classified by visual cross reference utilizing the user interface153and the classified document360can be transmitted for production, as illustrated at block470. The user actions342, while classifying the document305, can be monitored and stored in the training database340, as indicated at block480. Simultaneously, the user classifications and extraction models can be developed by the independent sub-system330, as indicated at block490. If the acceptability threshold value is met, the automated processing engine350automatically classifies the incoming document305, as indicated at block430.

A determination can then be made whether the document305is classified successfully, as illustrated at block440. If the document305is classified successfully, then the classified document360can be transmitted for production, as depicted at block460. Otherwise, the classification exceptional cases can be handled manually, as indicated at block450. Thereafter, the process can be continued from block470. As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention that may be embodied in various and alternative forms. The specific structural details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for the claims and/or as a representative basis for teaching one skilled in the art to variously employ the present invention.

It is believed that by utilizing the system and approach described herein, the training and configuration steps typically required for the document imaging classification and extraction system300can be automated. The method400avoids the need for costly and time consuming training prior to classification and can also avoid specialized skills required to perform the training. Such an approach allows for continual cost savings over time as the automated process engine quality improves with constant refinement from operator feedback.

Programs defining functions on the present invention can be delivered to a data storage system or a computer system via a variety of signal-bearing media, which include, without limitation, non-writable storage media (e.g., COD-ROM), writable storage media (e.g., hard disk drive, read/write CD ROM, optical media), system memory such as, but not limited to, Random Access Memory (RAM), and communication media such as computer and telephone networks including Ethernet, the Internet, wireless networks, and like network systems. It should be understood, therefore, that such signal-bearing media when carrying or encoding computer readable instructions that direct method functions in the present invention, represent alternative embodiments of the present invention. Further, it is understood that the present invention may be implemented by a system having means in the form of hardware, software, or a combination of software and hardware as described herein or their equivalent. Thus, the method400described herein can be deployed as process software in the context of a computer system or data-processing system as that depicted inFIGS. 1-2.