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They represent important stages in the printing of a document, but are invisible to the average user and unimportant for our purposes here, so they won't be discussed. The next 3 boxes, Art, Bleed, and Trim, have special meaning to printers. But it is still very important to page geometry, as will be explained below.
The Media Box doesn't have quite the same importance for an interactive document displayed on the screen. And all the other bounding boxes are inside this one. Originally this meant the paper size the page was to be printed on. Horizontal, or X, coordinates increase to the rights and vertical, or Y, coordinates increase towards the top (Figure 1)įigure 2 - The different Page Boxes that define a page's boundaries. The origin, or 0,0 point is located in the bottom left hand corner of the page. The units of User Space are called "points" and there are 72 points/inch.
And in fact that's a good way to think about it. This is a flat 2- dimensional space, just like a piece of paper.
The coordinate system on a PDF page is called User Space.
Automation tools that demonstrate page geometry operations. Swat the Fly Game (Variation on Bouncing Button). 2D Matrix Mulitplier (Discussed in Converting Coordinates). Sample Files that demonstrate page geometry operations. Finding Words, and Handling Word Locations. And is also very useful for form scripting. So understanding page coordinates is critical to many common automation activities. Skills that iterate over images, such as OCR and image analysis, expect normalized images.Page coordinates are used to add fields and annotations to a page, move fields and annotations, resize page boundaries, locate words on a page, and for any other operation that involves page geometry. Metadata adjustments are captured in a complex type created for each image. For images that have metadata on orientation, image rotation is adjusted for vertical loading. Large images are resized to a maximum height and width to make them uniform and consumable during skillset processing. Image normalization includes the following operations: As a developer, you enable image normalization by setting the "imageAction" parameter in indexer configuration. This second step occurs automatically and is internal to indexer processing. Image processing requires image normalization to make images more uniform for downstream processing. Extracted text is queued for text processing, if applicable. Extracted images are queued for image processing. Review service tier limits to make sure that your source data is under maximum size and quantity limits for indexers and enrichment.Įxtracting images from the source content files is the first step of indexer processing. Alternatively, you can authenticate using Azure Active Directory (Azure AD) or connect as a trusted service.Ĭreate a data source of type "azureblob" that connects to the blob container storing your files. If you're using a full access connection string that includes a key, the key gives you permission to the content. There are three main tasks related to retrieving images from a blob container:Įnable access to content in the container. If there are more than 1000 images in a document, the first 1000 will be extracted and a warning will be generated.Īzure Blob Storage is the most frequently used storage for image processing in Cognitive Search. A maximum of 1000 images will be extracted from a given document. Images are either standalone binary files or embedded in documents (PDF, RTF, and Microsoft application files).
Image analysis supports JPEG, PNG, GIF, and BMP. Image processing is indexer-driven, which means that the raw inputs must be in a supported data source. Optionally, you can define projections to accept image-analyzed output into a knowledge store for data mining scenarios.
A search index with fields to receive the analyzed text output, plus output field mappings in the indexer that establish association. A skillset with built-in or custom skills that invoke OCR or image analysis. A search indexer, configured for image actions.