Abstract: Various technologies related to generating and applying content retrieval rules are described herein. A content retrieval rule maps a combination of a query and a context to one of a query reformulation or content. The content retrieval rule is learned from search logs of a search engine, and is applied when the query having the context is received at the search engine.

Abstract: Various technologies related to generating and applying content retrieval rules are described herein. A content retrieval rule maps a combination of a query and a context to one of a query reformulation or content. The content retrieval rule is learned from search logs of a search engine, and is applied when the query having the context is received at the search engine.

Abstract: Various technologies related to generating and applying content retrieval rules are described herein. A content retrieval rule maps a combination of a query and a context to one of a query reformulation or content. The content retrieval rule is learned from search logs of a search engine, and is applied when the query having the context is received at the search engine.

Abstract: The disclosed architecture enables user feedback in the form of gestures, and optionally, voice signals, of one or more users, to interact with a search engine framework. For example, document relevance, document ranking, and output of the search engine can be modified based on the capture and interpretation of physical gestures of a user. The recognition of a specific gesture is detected based on the physical location and movement of the joints of a user. The architecture captures emotive responses while navigating the voice-driven and gesture-driven interface, and indicates that appropriate feedback has been captured. The feedback can be used to alter the search query, personalize the response using the feedback collected through the search/browsing session, modifying result ranking, navigation of the user interface, modification of the entire result page, etc., among many others.

Abstract: Discriminating between documents scanned in a batch scanning process is achieved based on various analyses of the constituent document pages. The data provided by the various analyses are compared with each other to determine whether successive pages belong to the same document. Scanned documents result in a page sequence that is analyzed to extract one or more feature attributes for each page. The feature attributes are provided to a feature comparison process in order to assess the similarity of successive pages. If a sufficient likelihood of similarity is found, the compared pages are deemed to be from the same document; otherwise, they are deemed to be from different documents, indicating the existence of a document break. Based on the document breaks, separate scan files may be established. In this manner, the present invention represents eliminates the requirement of user intervention.

Abstract: Discriminating between documents scanned in a batch scanning process is achieved based on various analyses of the constituent document pages. The data provided by the various analyses are compared with each other to determine whether successive pages belong to the same document. Scanned documents result in a page sequence that is analyzed to extract one or more feature attributes for each page. The feature attributes are provided to a feature comparison process in order to assess the similarity of successive pages. If a sufficient likelihood of similarity is found, the compared pages are deemed to be from the same document; otherwise, they are deemed to be from different documents, indicating the existence of a document break. Based on the document breaks, separate scan files may be established. In this manner, the present invention represents eliminates the requirement of user intervention.

Abstract: Discriminating between documents scanned in a batch scanning process is achieved based on various analyses of the constituent document pages. The data provided by the various analyses are compared with each other to determine whether successive pages belong to the same document. Scanned documents result in a page sequence. The page sequence is then analyzed to extract one or more features attributes for each page. The feature attributes are provided to a feature comparison process in order to assess the similarity of successive pages. If a sufficient likelihood of similarity is found, then the compared pages are deemed to be from the same document; otherwise, they are deemed to be from different documents, indicating the existence of a document break. Through the display of the page sequence, a user may optionally modify the location of one or more document breaks. Based on the document breaks, separate scan files may be established.

Abstract: Described is a system and method for improving string matching in a noisy channel environment. The invention provides a method for identifying string candidates and analyzing the probability that the string candidate matches a user-defined string. In one implementation, a find engine receives a query string, converts an image file into a textual file, and identifies each instance of the query string in the textual file. The find engine identifies candidates within the textual file that may match the query string. The find engine refers to a confusion table to help identify whether candidates that are near matches to the query string are actually matches to the query string but for a common recognition error. Candidates meeting a probability threshold are identified as matches to the query string. The invention further provides for analysis options including word heuristics, language models, and OCR confidences.

Abstract: Described is a system and method for improving string matching in a noisy channel environment. The invention provides a method for identifying string candidates and analyzing the probability that the string candidate matches a user-defined string. In one implementation, a find engine receives a query string, converts an image file into a textual file, and identifies each instance of the query string in the textual file. The find engine identifies candidates within the textual file that may match the query string. The find engine refers to a confusion table to help identify whether candidates that are near matches to the query string are actually matches to the query string but for a common recognition error. Candidates meeting a probability threshold are identified as matches to the query string. The invention further provides for analysis options including word heuristics, language models, and OCR confidences.