Abstract: Architecture that comprises features that enable smart searches along a route based on time to entities along the route and off the route, a user experience (UX) that showcases rich entities along the route, saving, sharing and editing capabilities across devices and users, smart ranking and filtering of entities, and user preferences and digital personal assistant interaction. After an itinerary is built, the itinerary can be saved for future use and/or shared with friends and/or other devices. Users can change the itinerary on-the-go by searching and adding entities, at a later time, for example. A digital personal audio assistant can be utilized to provide guidance based on the itinerary, and ask users if they want to take a particular exit to visit some desired location (e.g., place of interest).

Abstract: Methods and systems for generating and storing entity chain information, and for responding to search queries according to the entity chain information is presented. As a service obtains information regarding geographic entities, a plurality of entity records corresponding to each of a plurality of geographic entities is created (or updated) in an entity store. The service then analyzes the plurality of geographic entities (via the entity information in each of the entity records) to identify geographic entities that belong to an entity chain. Information regarding the identified entity chains are then also stored in the entity store.

Abstract: Architecture that comprises features that enable smart searches along a route based on time to entities along the route and off the route, a user experience (UX) that showcases rich entities along the route, saving, sharing and editing capabilities across devices and users, smart ranking and filtering of entities, and user preferences and digital personal assistant interaction. After an itinerary is built, the itinerary can be saved for future use and/or shared with friends and/or other devices. Users can change the itinerary on-the-go by searching and adding entities, at a later time, for example. A digital personal audio assistant can be utilized to provide guidance based on the itinerary, and ask users if they want to take a particular exit to visit some desired location (e.g., place of interest).

Abstract: Page segmentation in an optical character recognition process is performed to detect textual objects and/or image objects. Textual objects in an input gray scale image are detected by selecting candidates for native lines which are sets of horizontally neighboring connected components (i.e., subsets of image pixels where each pixel from the set is connected with all remaining pixels from the set) having similar vertical statistics defined by values of baseline (the line upon which most text characters “sit”) and mean line (the line under which most of the characters “hang”). Binary classification is performed on the native line candidates to classify them as textual or non-textual through examination of any embedded regularity. Image objects are indirectly detected by detecting the image's background using the detected text to define the background. Once the background is detected, what remains (i.e., the non-background) is an image object.

Abstract: An electronic model of the image document is created by undergoing an OCR process. The electronic model includes elements (e.g., words, text lines, paragraphs, images) of the image document that have been determined by each of a plurality of sequentially executed stages in the OCR process. The electronic model serves as input information which is supplied to each of the stages by a previous stage that processed the image document. A graphical user interface is presented to the user so that the user can provide user input data correcting a mischaracterized item appearing in the document. Based on the user input data, the processing stage which produced the initial error that gave rise to the mischaracterized item corrects the initial error. Stages of the OCR process subsequent to this stage then correct any consequential errors arising in their respective stages as a result of the initial error.

Abstract: Page segmentation in an optical character recognition process is performed to detect textual objects and/or image objects. Textual objects in an input gray scale image are detected by selecting candidates for native lines which are sets of horizontally neighboring connected components (i.e., subsets of image pixels where each pixel from the set is connected with all remaining pixels from the set) having similar vertical statistics defined by values of baseline (the line upon which most text characters “sit”) and mean line (the line under which most of the characters “hang”). Binary classification is performed on the native line candidates to classify them as textual or non-textual through examination of any embedded regularity. Image objects are indirectly detected by detecting the image's background using the detected text to define the background. Once the background is detected, what remains (i.e., the non-background) is an image object.